Forex Förutsägelse Svm

Stödvektor för regression Stödvektormetoden kan också tillämpas vid regressionsändring, behåller alla huvudfunktioner som karakteriserar maximal marginalalgoritm: en icke-linjär funktion lärs av en linjär inlärningsmaskin i ett kärninducerat funktionsutrymme medan systemets kapacitet styrs av en parameter som inte är beroende av rymdens dimensioner. Cristianini och Shawe-Taylor (2000) I SVM är grundtanken att kartlägga data x till ett högdimensionellt funktionsutrymme F via en olinjär kartläggning. och att göra linjär regression i detta utrymme (jfr Boser et al. (1992) Vapnik (1995)). Mest refererade bibliografi CAO, Lijuan, Support vektormaskiner experter för prognoser för tidsserier Simuleringen visar att SVMs experter uppnår signifikant förbättring av generaliseringsprestandan jämfört med de enskilda SVMs-modellerna. Dessutom konvergerar SVMs-experterna snabbare och använder färre stödvektorer. Cao (2002) GAO, J. B. S. R. GUNN och C. J. HARRIS, medelfältmetod för stödvektorns maskinregression Denna uppsats behandlar två ämnen. Först ska vi visa hur regressionsproblemet med stöd vektor maskin (SVM) kan lösas som den maximala en posteriori förutsägelsen i Bayesian-ramen. Den andra delen beskriver en approximationsteknik som är användbar för att utföra beräkningar för SVM: er baserat på den genomsnittliga fältalgoritmen som ursprungligen föreslogs i statistiska fysiken hos oordningade system. En fördel är att den hanterar bakre medelvärden för Gaussian process som inte är analytiskt töjbara. Gao, Gunn och Harris (2002) GUNN, S. Stöd vektormaskiner för klassificering och regression. ISIS Technical Report, 1998. Citerad av 164 HARLAND, ZAC, med hjälp av vektormaskiner för handel med aluminium på LME. I det här dokumentet beskrivs och utvärderas användningen av stödvektorregression för att handla det tre månader långa kontraktet om futures i aluminium på London Metal Exchange under perioden juni 1987 till november 1999. Support Vector Machine är en maskininlärningsmetod för klassificering och regression och är snabb ersätta neurala nätverk som det valfria verktyget för prediktions - och mönsterigenkänningsuppgifter, främst på grund av deras förmåga att generalisera sig väl på osynliga data. Algoritmen grundar sig på idéer som härrör från statistisk inlärningsteori och kan förstås intuitivt inom en geometrisk ram. I det här dokumentet använder vi stödvektorregression för att utveckla ett antal handelsunderlag som tillsammans resulterar i en slutlig modell som uppvisar över genomsnittlig avkastning på grund av provdata, vilket ger några bevis för att aluminiumpriset är mindre än effektivt. Huruvida dessa ineffektiviteter kommer att fortsätta i framtiden är okänt. Harland HONG, Dug Hun, Changha HWANG, Stöd vektor fuzzy regression maskiner Support vektor maskin (SVM) har varit mycket framgångsrik i mönsterigenkänning och funktion estimationproblem. I detta dokument introducerar vi användningen av SVM för multivariata fuzzy linjära och olinjära regressionsmodeller. Genom att använda den grundläggande idén som ligger till grund för SVM för multivariate fuzzy regressions ger beräkningseffektiviteten att få lösningar. Hong och Hwang M220LLER, K.-R. et al. Använda supportvektorer för tidsserien Prediction Support Vector Machines används för prognos av tidsserier och jämförs med radialbaserade funktionsnät. Vi använder två olika kostnadsfunktioner för supportvektorer: träning med (i) en epsilon okänslig förlust och (ii) Hubers robust förlustfunktion och diskutera hur man väljer normaliseringsparametrarna i dessa modeller. Två applikationer beaktas: data från (a) ett bråkigt Mackey-Glass-system (normalt och enhetligt ljud) och (b) Santa Fe Time Series-tävlingen (set D). I båda fallen visar Support Vector Machines en utmärkt prestanda. I fall (b) förbättrar Support Vector-tillvägagångssättet det mest kända resultatet på referensvärdet av 29.Muller et al. (2000) PONTIL, Massimiliano, Sayan MUKHERJEE och Federico GIROSI, på bullermodellen för stöd Vector Machine Regression Pontil, Mukherjee och Girosi (1998) SMOLA, Alex J. och Bernhard SCH214LKOPF, En handledning om supportvektorregression Smola och Scholkopf ) Citerad av 309Support Vector Machines: Finansiella applikationer Listade i citatordningar per år, högst uppe. Senast uppdaterad september 2006. PANG, Bo, Lillian LEE och Shivakumar VAITHYANATHAN, 2002. Thumbs up Sentiment Classification using Machine Learning Techniques. I: EMNLP 02: Förlopp av ACL-02-konferensen om empiriska metoder i naturprocessbehandling - volym 10. sidorna 79--86. Citerad av 154 (36.66 år) Sammanfattning: Vi anser att problemet med att klassificera dokument inte är ämne, men genom övergripande sentiment, t. ex. avgöra huruvida en granskning är positiv eller negativ. Med hjälp av filmrecensioner som data finner vi att standard maskininlärningsteknik definitivt överträffar mänskliga basplinterar. De tre maskininlärningsmetoderna vi använde (Naive Bayes, maximal entropiklassificering och stödvektormaskiner) fungerar emellertid inte lika bra på sentimentklassificering som på traditionell ämnesbaserad kategorisering. Vi avslutar med att undersöka faktorer som gör problem med problematiken mer utmanande. fann att med hjälp av filmöversikter som data har standardmaskins inlärningsteknik definitivt överträffat människoproducerade baslinjer. De fann emellertid också att de tre maskininlärningsmetoderna de använde (Naive Bayes, maximal entropiklassificering och stödvektormaskiner) inte fungerade lika bra på sentimentklassificering som på traditionell ämnesbaserad kategorisering. VAN GESTEL, Tony, et al. . 2001. Finansiell tidsserieförutsägelse med hjälp av minst kvadrater Stöd vektorgrafik inom bevisramen. IEEE-transaktioner på neurala nätverk. Volym 12, Nummer 4, Juli 2001, Sidor 809-821. Citationstecken 77 (14.82 år) Sammanfattning: Den bayesiska evidensramen tillämpas i detta papper till regression i minsta kvadrera stödvektor maskin (LS-SVM) för att utgå från olinjära modeller för att förutsäga en ekonomisk tidsserie och den därmed sammanhängande volatiliteten. På den första nivån av inferens är en statistisk ram relaterad till LS-SVM-formuleringen som gör att man kan inkludera tidens varierande volatilitet på marknaden genom ett lämpligt val av flera hyperparametrar. Modellens hyperparametrar utgår från den andra nivån av inferensen. De härledda hyperparametrarna, som är relaterade till volatiliteten, används för att konstruera en volatilitetsmodell inom bevisramen. Modelljämförelse utförs på den tredje nivån av inferens för att automatiskt ställa in parametrarna för kärnfunktionen och för att välja relevanta ingångar. LS-SVM-formuleringen tillåter att man härledar analytiska uttryck i funktionsutrymmet och praktiska uttryck erhålles i dubbelrummet som ersätter den inre produkten med den relaterade kärnfunktionen med användning av Mercers-steget. De förutspådda resultatprestationerna som erhållits vid förutspårningen av den veckovisa 90-dagars fakturafrekvensen och de dagliga DAX30 slutkurserna visar att signifikanta utfall av provtecken förutsägelser kan göras med avseende på pesaran-timmerman testet statistiskt tillämpat den bayesiska bevisramen Minst kvadrerar stödvektormaskinens (LS-SVM) regression för att förutsäga den veckovisa 90-dagars räntesatsen och de dagliga DAX30 slutkurserna. TAY, Francis E. H. och Lijuan CAO, 2001. Användning av stödvektormaskiner i prognostisering av finansiella tidsserier. Omega: International Journal of Management Science. Volym 29, utgåva 4, augusti 2001, sidor 309-317. Citerad av 67 (12.89 år) Sammanfattning: Detta dokument handlar om tillämpningen av en ny neuralt nätverksteknik, stödvektormaskin (SVM), i prognoser för finansiella tidsserier. Syftet med detta dokument är att undersöka möjligheten för SVM i prognoser för ekonomisk tidsserier genom att jämföra den med ett neuralsnät med flera lager (BP). Fem realtidskontrakt som samlas från Chicago Mercantile Market används som dataset. Experimentet visar att SVM överträffar BP-neuralt nätverk baserat på kriterierna för normaliserat medelvärdefel (NMSE), medelvärdefel (MAE), riktningssymmetri (DS) och vägd riktningssymmetri (WDS). Eftersom det inte finns något strukturerat sätt att välja de fria parametrarna hos SVM, undersöks variationen i prestanda med avseende på de fria parametrarna i denna studie. Analys av försöksresultaten visade att det är fördelaktigt att tillämpa SVMs för att prognostisera finansiella tidsserier. Konstaterade att en SVM överträffade ett neuralsnät med flera lager för återbredning (BP) på fem realtidskontrakt från Chicago Mercantile Market. TAY, Francis E. H. och L. J. CAO, 2002. Modifierade stödvektormaskiner i prognostisering av finansiella tidsserier. Neurocomputing. Volym 48, Issues 1-4, October 2002, Sidor 847-861. Citerad av 54 (12.86 år) Sammanfattning: I det här dokumentet föreslås en modifierad version av stödvektorns maskiner, kallad C-encoding-stödvektormaskin, för att modellera icke-stationära finansiella tidsserier. C-inkommande stödvektormaskiner erhölls genom en enkel modifiering av den regulariserade riskfunktionen i stödvektormaskiner, varigenom de senaste 949-okänsliga felen straffas hårdare än de avlägsna 949-okänsliga felen. Denna procedur är baserad på den förutvarande kunskapen att i de icke-stationära finansiella tidsserierna förändras beroendeet av ingångsvariablerna och utgångsvariabeln gradvis över tiden, speciellt kan de senaste uppgifterna ge mer viktig information än de avlägsna tidigare data. I försöket testas C-inkassande stödvektormaskiner med hjälp av tre reala terminer insamlade från Chicago Mercantile Market. Det visas att de C-inkluderande stödvektormaskinerna med de faktiskt beställda provdataen konsekvent förutser bättre än standard-stödvektormaskinerna, med den värsta prestandan när de omvända beställda provdata används. Vidare använder de C-inkluderande stödvektormaskinerna färre stödvektorer än de hos standard-stödvektormaskinerna, vilket resulterar i en sparsigare representation av lösningsutvecklade C-inkluderande stödvektormaskiner, vilka straffar de senaste 949-okänsliga felen är tungare än avlägsna 949-okänsliga fel, och fann att de förutspår bättre än vanliga SVMs på tre reala terminer samlade från Chicago Mercantile Market. HUANG, Zan, et al. . 2004. Kreditvärderingsanalys med stödvektorer och neurala nätverk: en jämförande studie på marknaden. Beslutsstödsystem. Volym 37, utgåva 4 (september 2004), sidor 543-558. Citerad av 21 (9.55 år) Sammanfattning: Analys av företagsbedömning av kreditbetyg har lockat massor av forskningsintressen i litteraturen. Tidigare studier har visat att metoder för artificiell intelligens (AI) uppnådde bättre prestanda än traditionella statistiska metoder. Denna artikel introducerar en relativt ny maskininlärningsteknik, stöd vektormaskiner (SVM), till problemet i försök att ge en modell med bättre förklarande effekt. Vi använde backpropagation neuralt nätverk (BNN) som referens och uppnådde förutsägelsesnoggrannhet kring 80 för både BNN - och SVM-metoder för USA och Taiwan. Dock observerades endast liten förbättring av SVM. En annan riktning av forskningen är att förbättra tolkbarheten hos de AI-baserade modellerna. Vi tillämpade senaste forskningsresultat i tolkning av neurala nätverksmodeller och erhållit relativ betydelse för de ingående finansiella variablerna från de neurala nätverksmodellerna. Baserat på dessa resultat genomförde vi en jämförande marknadsanalys på skillnaderna mellan bestämningsfaktorer i USA och Taiwan markets. applied backpropagation neurale nätverk och SVMs till prognos för företagens kreditvärdighet för USA och Taiwan marknader och fann att resultaten var jämförbara (båda var överlägsen logistisk regression), med SVM något bättre. CAO, Lijuan, 2003. Support vektor maskiner experter för prognoser för tidsserier. Neurocomputing. Volym 51, april 2003, sidor 321-339. Citerad av 29 (9.08 år) Sammanfattning: I detta dokument föreslås att man använder experter för stödvektormaskiner (SVMs) för prognoser för tidsserier. De generaliserade SVM-experterna har en tvåstegs neuralt nätverkarkitektur. I det första steget används självorganiserande funktionskarta (SOM) som en grupperingsalgoritm för att partitionera hela inmatningsutrymmet i flera ojämna områden. En trädstrukturerad arkitektur antas i partitionen för att undvika problemet med att förutbestämma antalet partitionerade regioner. Därefter konstrueras flera SVM, även kallade SVM-experter, som bäst passar partitionerade regioner, genom att hitta den mest lämpliga kärnfunktionen och de optimala fria parametrarna för SVM. Sunspot-data, Santa Fe-dataset A, C och D, och de två byggnadsdatasätten utvärderas i experimentet. Simuleringen visar att SVMs experter uppnår en signifikant förbättring av generaliseringsprestandan jämfört med de enskilda SVM-modellerna. Dessutom konvergerar SVM-experterna också snabbare och använder färre stödvektorer. Visar att deras metod för SVM-experter uppnådde betydande förbättringar över enstaka SVM-modeller när de tillfördes Santa Fe-datamängden C (högfrekventa växelkurser mellan schweiziska francen och Amerikanska dollar). KIM, Kyoung-jae, 2003. Finansiella prognoser för tidsserier med hjälp av vektorvektorer. Neurocomputing. Volym 55, Issues 1-2 (September 2003), Sidor 307-319. Citerad av 28 (8.76 år) Sammanfattning: Stödvektormaskiner (SVM) är lovande metoder för förutsägelse av finansiella tidsserier, eftersom de använder en riskfunktion som består av empiriska fel och en regulariserad term som härrör från principen om minimering av strukturella risker . Denna studie gäller SVM för att förutsäga aktiekursindex. Dessutom undersöker denna studie möjligheten att tillämpa SVM i finansiell prognostisering genom att jämföra den med återbredda neurala nätverk och fallbaserade resonemang. De experimentella resultaten visar att SVM ger ett lovande alternativ till aktiemarknadsprediktionen. Det konstateras att SVMs överträffade neuroplaneringsnätverk och fallbaserad resonemang när den används för att prognostisera det dagliga Korea Composite Stock Price Index (KOSPI). SHIN Kyung-Shik, Taik Soo LEE och Hyun-jung KIM, 2005. En ansökan av stödvektormaskiner i konkursmodell. Expert Systems with Applications. Volym 28, Utgåva 1, Januari 2005, Sidor 127-135. Citerad av 8 (6.67 år) Sammanfattning: Denna studie undersöker effekten av att applicera stödvektormaskiner (SVM) till konkursprognosproblem. Även om det är ett välkänt faktum att det bakre propagationsnorala nätverket (BPN) fungerar bra i mönsterkänningsuppgifter, har metoden vissa begränsningar eftersom det är en konst att hitta en lämplig modellstruktur och optimal lösning. Dessutom behöver du ladda så många av träningssatsen som möjligt till nätverket för att söka efter nätets vikt. Å andra sidan, eftersom SVM fångar geometriska egenskaper hos funktionsutrymmet utan att avleda vikter av nätverk från träningsdata, kan den extrahera den optimala lösningen med den lilla träningsuppsättningen. I den här studien visar vi att den föreslagna klassificeringen av SVM-angrepp överträffar BPN till problemet med företagets konkursförutsägelse. Resultaten visar att SVM: s noggrannhet och generaliseringsprestanda är bättre än BPNs, eftersom träningsuppsättningen blir mindre. Vi undersöker också effekten av variationen i prestanda med avseende på olika parametervärden i SVM. Dessutom undersöker och sammanfattar vi de flera överlägsen punkterna i SVM-algoritmen jämfört med BPN. demonstrated att SVMs fungerar bättre än neuroprogrammer för återbredning när de tillämpas på företagets konkursförutsägelse. CAO, L. J. och Francis E. H. TAY, 2003. Support Vector Machine med adaptiva parametrar i Financial Time Series Prognos. IEEE-transaktioner på neurala nätverk. Volym 14, Utgåva 6, November 2003, Sidor 1506-1518. Citerad av 20 (6.25 år) Sammanfattning: En ny typ av inlärningsmaskin som kallas stödvektormaskin (SVM) har fått ökande intresse för områden som sträcker sig från sin ursprungliga tillämpning i mönsterigenkänning till andra tillämpningar, såsom regressionsuppskattning på grund av dess anmärkningsvärda generaliseringsprestanda . Detta dokument handlar om tillämpningen av SVM i prognostisering av finansiella tidsserier. Möjligheten att tillämpa SVM i ekonomisk prognos undersöks först genom att jämföra den med neuralsnätet för multipel backspridning (BP) och det reguljära radialbasisfunktionen (RBF) neuralt nätverk. SVM: s variabilitet med avseende på de fria parametrarna undersöks experimentellt. Adaptiva parametrar föreslås då genom att integrera icke-stationaritet i finansiella tidsserier i SVM. Fem realtidskontrakt samlade från Chicago Mercantile Market används som dataset. Simuleringen visar att SVM överträffar BP neurala nätet i de tre metoderna, och det finns jämförbar generaliseringsprestanda mellan SVM och det reguljära RBF neurala nätverket. Vidare har de fria parametrarna hos SVM en stor effekt på generaliseringsprestandan. SVM med adaptiva parametrar kan båda uppnå högre generaliseringsprestanda och använda färre stödvektorer än standard SVM i finansiell prognos. Användes ett SVM, ett neuralsnät för flera lager och propagation (RBF) med en normaliserad radiell grundfunktion (RBF) för att förutsäga Fem realtidskontrakt samlades från Chicago Mercantile Market. Resultaten visade att SVM och det regulariserade RBF neurala nätverket var jämförbara och båda överträffade BP neurala nätverket. CAO, Lijuan och Francis E. H. TAY, 2001. Finansiell prognos med hjälp av vektorgrafik. Neural Computing Amp Applications. Volym 10, nummer 2 (maj 2001), sidor 184-192. Citerad av 26 (5.00 år) Sammanfattning: Användningen av Support Vector Machines (SVMs) studeras i finansiell prognos genom att jämföra den med en multi-layer perceptron utbildad av Back Propagation (BP) - algoritmen. SVMs prognostiserar bättre än BP baserat på kriterierna för normaliserad genomsnittlig kvadratfel (NMSE), Mean Absolute Error (MAE), Directional Symmetry (DS), Korrigera upp (CP) trend och Correct Down (CD) trend. SampP 500 daglig prisindex används som dataset. Eftersom det inte finns något strukturerat sätt att välja de fria parametrarna för SVM, undersöks generaliseringsfelet med avseende på de fria parametrarna för SVMs i detta experiment. Såsom illustreras i experimentet har de liten inverkan på lösningen. Analys av försöksresultaten visar att det är fördelaktigt att tillämpa SVMs för att prognostisera den finansiella tidsserien. Konstaterar att SVMs prognostiserar det dagliga prisindexet SampP 500 bättre än en flerlagsperceptron utbildad av Back Propagation (BP) - algoritmen. MIN, Jae H. och Young-Chan LEE, 2005. Konkursförutsägelse med hjälp av vektormaskin med optimalt val av kärnfunktionsparametrar. Expert Systems with Applications. Volym 28, Utgåva 4, Maj 2005, Sidor 603-614. Citerad av 6 (5.00 år) Sammanfattning: Konkursförutsägningen har dragit en hel del forskningsintressen i tidigare litteratur, och nya studier har visat att maskininlärningsteknik uppnådde bättre prestanda än traditionella statistiska. Detta papper gäller stödvektormaskiner (SVM) till konkursprognosproblemet i ett försök att föreslå en ny modell med bättre förklarande kraft och stabilitet. För att tjäna detta syfte använder vi en rutnätteknik med 5-faldig kryssvalidering för att få reda på de optimala parametervärdena för SVMs kärnfunktion. Dessutom, för att utvärdera prediktionsnoggrannheten hos SVM, jämför vi dess prestanda med dem för multipeldiskriminationsanalys (MDA), logistisk regressionsanalys (Logit) och trelagers helt anslutna back-propagation neurale nätverk (BPN). Experimentresultaten visar att SVM överträffar de andra metoderna. Det konstaterades att SVM: s överträffade multipeldiskriminationsanalys (MDA), logistisk regressionsanalys (Logit) och trelagers helt anslutna återbredda neurala nätverk (BPN). ABRAHAM, Ajith, Ninan Sajith PHILIP och P. SARATCHANDRAN, 2003. Modellera kaotiskt beteende av aktieindex med hjälp av intelligenta paradigmer. Neural, Parallel amp Scientific Computations. Volym 11, sidorna 143-160. Citerad av 10 (4.55 år) Sammanfattning: Användningen av intelligenta system för aktiemarknadsutsikter har etablerats i stor utsträckning. I det här dokumentet undersöker vi hur aktiemarknadernas till synes kaotiska beteende skulle kunna vara väl representerade genom att använda flera anslutningsparadigmer och mjukvaruteknik. För att visa de olika teknikerna ansåg vi Nasdaq-100 index på Nasdaq Stock Market SM och SP CNX NIFTY aktieindex. Vi analyserade 7 år8217s Nasdaq 100 huvudindexvärden och 4 år8217s NIFTY-indexvärden. I detta dokument undersöks utvecklingen av en tillförlitlig och effektiv teknik för att modellera aktiemarknadernas till synes kaotiska beteende. Vi ansåg ett konstgjort neuralt nätverk utbildat med hjälp av Levenberg-Marquardt-algoritmen, Support Vector Machine (SVM), Takagi-Sugeno neurofuzzy-modellen och ett Difference Boosting Neural Network (DBNN). I det här dokumentet förklaras kortfattat hur de olika anslutnings paradigmerna kan formuleras med olika inlärningsmetoder och sedan undersöka om de kan tillhandahålla den nödvändiga prestationsnivån, som är tillräckligt bra och robusta för att tillhandahålla en tillförlitlig prognosmodell för aktiemarknadsindex. Experimentresultat avslöjar att alla anslutna paradigmer som övervägs skulle kunna representera aktieindexbeteendet mycket exakt. Tillämpade fyra olika tekniker, ett konstgjort neuralt nätverk utbildat med hjälp av Levenberg-Marquardt-algoritmen, en stödvektormaskin, ett skillnadsstimulärt neuralt nätverk och en Takagi-Sugeno fuzzy inference-systemet lärt sig att använda en neural-nätverksalgoritm (neuro-fuzzy-modellen) till förutsägelsen av Nasdaq-100-indexet på Nasdaq Stock Market och SP CNX NIFTY aktieindex. Ingen teknik var tydligt överlägsen, men absurt, försöker de att förutse indexets absoluta värde, snarare än att använda loggen returnerar. YANG, Haiqin, Laiwan CHAN och Irwin KING, 2002. Stöd Vector Machine Regression för Volatile Stock Market Prediction. I: Intelligent datateknik och automatiserat lärande: IDEAL 2002. redigerad av Hujun Yin, et al. . sidorna 391--396, Springer. Citerad av 19 (4.52 år) Sammanfattning: Nyligen har Support Vector Regression (SVR) införts för att lösa regression och prediksionsproblem. I det här dokumentet tillämpar vi SVR på ekonomiska förutsägelser. I synnerhet är de finansiella uppgifterna vanligtvis bullriga och den därmed sammanhängande risken är tidsvarierande. Därför är vår SVR-modell en förlängning av standard SVR som innehåller marginalanpassning. Genom att ändra SVR: s marginaler kan vi spegla förändringen i volatiliteten i de finansiella uppgifterna. Vidare har vi analyserat effekten av asymmetriska marginaler för att möjliggöra en minskning av risken för nackdelen. Våra experimentella resultat visar att användningen av standardavvikelsen för att beräkna en variabel marginal ger ett bra förutsägbart resultat vid prediktering av Hang Seng Index. tryck varierar marginalerna i SVM-regression för att återspegla förändringen i volatiliteten i finansiella data och analyserade också effekten av asymmetriska marginaler för att möjliggöra en minskning av risken för nackdelen. Det tidigare tillvägagångssättet gav det lägsta totala felet när man förutspådde det dagliga stängningskursen för Hong Kongs Hang Seng Index (HSI). HUANG, W. Y. NAKAMORI och S. Y. WANG, 2005. Prognos aktiemarknadsrörelse riktning med stöd vektor maskin. Datorer Operations Research. Volym 32, Utgåva 10, Sidor 2513-2522. (Oktober 2005) Citerad av 5 (4.18 år) Sammanfattning: Stödvektormaskinen (SVM) är en mycket specifik typ av inlärningsalgoritmer som kännetecknas av kapacitetsreglering av beslutsfunktionen, användningen av kärnfunktionerna och lösheten i lösningen. I det här dokumentet undersöker vi förutsägbarheten för finansiell rörelseriktning med SVM genom att prognostisera den veckovisa rörelseriktningen för NIKKEI 225-indexet. För att utvärdera SVMs prognosförmåga jämför vi dess prestanda med de av linjär diskriminerande analys, kvadratisk diskriminerande analys och Elman Backpropagation Neural Networks. Experimentresultaten visar att SVM överträffar de andra klassificeringsmetoderna. Vidare föreslår vi en kombinationsmodell genom att integrera SVM med de andra klassificeringsmetoderna. Kombinationsmodellen fungerar bäst bland alla prognosmetoder som sparar SVMs, Linjär diskriminantanalys, Quadratic Discriminant Analysis och Elman Backpropagation Neural Networks förmåga att prognostisera NIKKEI 225-indexet varje vecka och fann att SVM överträffade alla andra klassificeringsmetoder . Bättre fortfarande var en vägt kombination av modellerna. TRAFALIS, Theodore B. och Huseyin INCE, 2000. Stödvektor för regression och applikationer till finansiell prognos. I: IJCNN 2000: Förhandlingar av IEEE-INNS-ENNS internationella gemensamma konferens om neurala nätverk: Volym 6 redigerad av Shun-Ichi Amari, et al. . sidan 6348, IEEE Computer Society. Citationstecken 19 (3.06 år) Sammanfattning: Huvudsyftet med denna uppsats är att jämföra stödvågsmaskinen (SVM) som utvecklats av Vapnik med andra tekniker som Backpropagation och Radial Basis Function (RBF) Networks för finansiella prognoser. Teorin om SVM-algoritmen bygger på statistisk inlärningsteori. Utbildning av SVM leder till ett kvadratisk programmeringsproblem (QP). Preliminära beräkningsresultat för prissättning av aktiekurserna presenteras också försvunna SVMs med Backpropagation och Radial Basis Function (RBF) Nätverk genom att förutsäga IBM, Yahoo och America Online dagliga aktiekurser. Oddligt, genom att använda SVM för regression, försvann de en valideringsuppsättning, satt epsilon till noll, fast C och upprepade experimentet för olika fasta inställningar för kärnparametern, sigma, vilket gav upphov till flera resultat. CAO, Lijuan och Qingming GU, 2002. Dynamiska stödvektorer för icke-stationära prognoser för tidsserier. Intelligent dataanalys. Volym 6, Nummer 1, Sidor 67-83. Citerad av 12 (2.86 år) Sammanfattning: I det här dokumentet föreslås en modifierad version av stödvektormaskiner (SVM), kallade dynamiska stödvektormaskiner (DSVM), för att modellera icke-stationära tidsserier. DSVM: erna erhålls genom att inkorporera problemdomänkunskapen - icke-stationaritet av tidsserier i SVMs. Till skillnad från standard SVM som använder fasta värden för normaliseringskonstanten och rörstorleken i alla träningsdata, använder DSVM en exponentiellt ökande regulariseringskonstant och en exponentiellt minskande rörstorlek för att hantera strukturella förändringar i data. Den dynamiska normaliseringskonstanten och rörstorleken är baserad på förkunskapen att de senaste datapunkterna i de icke-stationära tidsserierna kan ge mer viktig information än avlägsna datapunkter. I experimentet utvärderas DSVM-värdena med både simulerade och reella dataset. Simuleringen visar att DSVM generaliserar bättre än standard SVM i prognoser för icke-stationära tidsserier. En annan fördel med denna modifiering är att DSVM: erna använder färre stödvektorer, vilket resulterar i en mindre fördelning av lösningen. Införlivar den förutvarande kunskapen att finansiella tidsserier är icke-stationära i sina dynamiska stödvektormaskiner (DSVM) och använder en exponentiellt ökande normaliseringskonstant och en exponentiellt minskande rörstorlek för att hantera strukturella förändringar i data utifrån antagandet att de senaste datapunkterna kan ge mer viktig information än avlägsna datapunkter. De drar slutsatsen att DSVM generaliserar bättre än vanliga SVM i prognoser för icke-stationära tidsserier, medan de också använder färre stödvektorer, vilket resulterar i en mindre fördelning av lösningen. TAY, Francis E. H. och L. J. CAO, 2002. 949-Descending Support Vector Maskiner för Financial Times Series Prognos. Neural Processing Letters 15 (2): 179-195. Citerad av 11 (2.62 år) Sammanfattning: I det här dokumentet föreslås en modifierad version av stödvektormaskiner (SVM), kallade 949-stigande stödvektormaskiner (949-DSVM), för att modellera icke-stationära finansiella tidsserier. 949-DSVM: erna erhålls genom att inkorporera problemdomänkunskap 8211 icke-stationaritet av finansiella tidsserier i SVMs. Till skillnad från de vanliga SVM: erna som använder ett konstant rör i alla träningsdata, använder 949-DSVMs ett adaptivt rör för att hantera strukturändringar i data. Experimentet visar att 949-DSVM generaliserar bättre än standard SVM i prognoser icke-stationära finansiella tidsserier. En annan fördel med denna modifiering är att 949-DSVM: erna konvergerar till färre stödvektorer, vilket resulterar i en sparsigare representation av lösningen. Innefattade problemdomänkunskapen om icke-stationaritet av finansiella tidsserier i SVM genom att använda ett adaptivt rör i deras så kallade epsilon-stigande stödvektormaskiner (epsilon-DSVM). Experiment visade att epsilon-DSVMs generaliserar bättre än vanliga SVM i prognoser för icke-stationära finansiella tidsserier och även konvergerar till färre stödvektorer, vilket resulterar i en mindre fördelning av lösningen. DEBNATH, Sandip och C. Lee GILES, 2005. En lärandebaserad modell för utskrift av nyhetsartiklar för att få förklarande meningar för händelser. I: K-CAP 821705: Förlopp av den tredje internationella konferensen om kunskapsupptagning. Sidor 189-190. Citerad av 2 (1.67 år) Sammanfattning: Metadatainformation spelar en avgörande roll för att öka dokumentorganiseringseffektiviteten och arkiverbarheten. Nyhetsmetadata innehåller DateLine. ByLine. HeadLine och många andra. Vi fann att HeadLine-informationen är användbar för att gissa temaet för nyhetsartikeln. I synnerhet för finansiella nyhetsartiklar fann vi att HeadLine sålunda kan vara särskilt användbart för att lokalisera förklarande meningar för större händelser som betydande förändringar i aktiekurserna. I det här dokumentet utforskar vi ett stöd för vektorbaserat inlärningsmetoder för att automatiskt extrahera HeadLine-metadata. Vi finner att klassificeringsnoggrannheten att hitta HeadLine s förbättras om DateLine s identifieras först. Vi använde sedan den extraherade HeadLine s för att initiera en mönster matchning av nyckelord för att hitta meningar som är ansvariga för historietema. Med hjälp av detta tema och en enkel språkmodell är det möjligt att hitta några förklarande meningar för någon betydande prisförändring. Visat ett nytt sätt att extrahera nyhetsmetadata HeadLines med hjälp av SVMs och använda dem för att hitta historiska teman för att få en meningsbaserad förklaring till ett lager prisförändring. Van GESTEL, Tony, et al. . 2003. En stöd vektor maskin tillvägagångssätt till kredit poäng. Bank en Financiewezen . Volume 2, March, Pages 73-82. Cited by 5 (1.56year) Abstract: Driven by the need to allocate capital in a profitable way and by the recently suggested Basel II regulations, financial institutions are being more and more obliged to build credit scoring models assessing the risk of default of their clients. Many techniques have been suggested to tackle this problem. Support Vector Machines (SVMs) is a promising new technique that has recently emanated from different domains such as applied statistics, neural networks and machine learning. In this paper, we experiment with least squares support vector machines (LS-SVMs), a recently modified version of SVMs, and report significantly better results when contrasted with the classical techniquespared four methodologies, Ordinary Least Squares (OLS), Ordinal Logistic Regression (OLR), the Multilayer Perceptron (MLP) and least squares support vector machines (LS-SVMs) when applied to credit scoring. The SVM methodology yielded significantly and consistently better results than the classical linear rating methods. FAN, Alan and Marimuthu PALANISWAMI, 2000. Selecting Bankruptcy Predictors Using a Support Vector Machine Approach. IJCNN 2000: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Volume 6 . edited by Shun-Ichi Amari et al. . page 6354. Cited by 9 (1.45year) Abstract: Conventional Neural Network approach has been found useful in predicting corporate distress from financial statements. In this paper, we have adopted a Support Vector Machine approach to the problem. A new way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is provided using three classical corporate distress models and an alternative model based on the SVM approach. use SVMs to select bankruptcy predictors, and provide a comparative study. TAY, Francis Eng Hock and Li Juan CAO, 2001. Improved financial time series forecasting by combining Support Vector Machines with self-organizing feature map. Intelligent Data Analysis . Volume 5, Number 4, Pages 339-354. Cited by 7 (1.35year) Abstract: A two-stage neural network architecture constructed by combining Support Vector Machines (SVMs) with self-organizing feature map (SOM) is proposed for financial time series forecasting. In the first stage, SOM is used as a clustering algorithm to partition the whole input space into several disjoint regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVMs, also called SVM experts, that best fit each partitioned region are constructed by finding the most appropriate kernel function and the optimal learning parameters of SVMs. The Santa Fe exchange rate and five real futures contracts are used in the experiment. It is shown that the proposed method achieves both significantly higher prediction performance and faster convergence speed in comparison with a single SVM modelbined SVMs with a self-organizing feature map (SOM) and tested the model on the Santa Fe exchange rate and five real futures contracts. They showed that their proposed method achieves both significantly higher prediction performance and faster convergence speed in comparison with a single SVM model. SANSOM, D. C. T. DOWNS and T. K. SAHA, 2003. Evaluation of support vector machine based forecasting tool in electricity price forecasting for Australian national electricity market participants. Journal of Electrical Electronics Engineering, Australia . Vol 22, No. 3, Pages 227-234. Cited by 5 (1.19year) Abstract: In this paper we present an analysis of the results of a study into wholesale (spot) electricity price forecasting utilising Neural Networks (NNs) and Support Vector Machines (SVM). Frequent regulatory changes in electricity markets and the quickly evolving market participant pricing (bidding) strategies cause efficient retraining to be crucial in maintaining the accuracy of electricity price forecasting models. The efficiency of NN and SVM retraining for price forecasting was evaluated using Australian National Electricity Market (NEM), New South Wales regional data over the period from September 1998 to December 1998. The analysis of the results showed that SVMs with one unique solution, produce more consistent forecasting accuracies and so require less time to optimally train than NNs which can result in a solution at any of a large number of local minima. The SVM and NN forecasting accuracies were found to be very similar. evaluated utilising Neural Networks (NNs) and Support Vector Machines (SVM) for wholesale (spot) electricity price forecasting. The SVM required less time to optimally train than the NN, whilst the SVM and NN forecasting accuracies were found to be very similar. ABRAHAM, Ajith and Andy AUYEUNG, 2003. Integrating Ensemble of Intelligent Systems for Modeling Stock Indices. In: Proceedings of 7th International Work Conference on Artificial and Natural Neural Networks, Part II . Lecture Notes in Computer Science, Volume 2687, Jose Mira and Jose R. Alverez (Eds.), Springer Verlag, Germany, pp. 774-781, 2003. Cited by 3 (0.94year) Abstract: The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well-represented using ensemble of intelligent paradigms. To demonstrate the proposed technique, we considered Nasdaq-100 index of Nasdaq Stock Market SM and the SampP CNX NIFTY stock index. The intelligent paradigms considered were an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. The different paradigms were combined using two different ensemble approaches so as to optimize the performance by reducing the different error measures. The first approach is based on a direct error measure and the second method is based on an evolutionary algorithm to search the optimal linear combination of the different intelligent paradigms. Experimental results reveal that the ensemble techniques performed better than the individual methods and the direct ensemble approach seems to work well for the problem considered. considered an artificial neural network trained using Levenberg-Marquardt algorithm, a support vector machine, a Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network for predicting the NASDAQ-100 Index of The Nasdaq Stock Market and the SP CNX NIFTY stock index. They concluded that an ensemble of the intelligent paradigms performed better than the individual methods. YANG, Haiqin, et al. . 2004. Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression. In: Neural Information Processing: Research and Development . edited by Jagath Chandana Rajapakse and Lipo Wang, Springer-Verlag. Cited by 2 (0.91year) Abstract: Recently, Support Vector Regression (SVR) has been applied to financial time series prediction. The financial time series usually contains the characteristics of small sample size, high noise and non-stationary. Especially the volatility of the time series is time-varying and embeds some valuable information about the series. Previously, we had proposed to use the volatility in the data to adaptively change the width of the margin in SVR. We have noticed that up margin and down margin would not necessary be the same, and we also observed that their choice would affect the upside risk, downside risk and as well as the overall prediction performance. In this work, we introduce a novel approach to adopt the momentum in the asymmetrical margins setting. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average. used SVMs for regression with non-fixed and asymmetrical margin settings, this time with momentum, to predict the Hang Seng Index and Dow Jones Industrial Average. PAI, Ping-Feng and Chih-Sheng LIN, 2005. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega . Volume 33, Issue 6, December 2005, Pages 497-505. Cited by 1 (0.84year) Abstract: Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising. proposed a hybrid ARIMA and support vector machine model for stock price forecasting, and results looked very promising. ABRAHAM, Ajith, et al. . 2002. Performance Analysis of Connectionist Paradigms for Modeling Chaotic Behavior of Stock Indices. In: Second international workshop on Intelligent systems design and application . edited by Ajith Abraham, et al. . pages 181--186. Cited by 3 (0.71year) Abstract: The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketTM and the SP CNX NIFTY stock index. We analyzed 7 years Nasdaq 100 main index values and 4 years NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately. analysed the performance of an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN) when predicting the NASDAQ-100 Index of The Nasdaq Stock Market and the SP CNX NIFTY stock index. YANG, Haiqin, I. KING and Laiwan CHAN, 2002. Non-fixed and asymmetrical margin approach to stock market prediction using Support Vector Regression. In: ICONIP 02. Proceedings of the 9th International Conference on Neural Information Processing. Volume 3 . edited by Lipo Wang, et al. . pages 1398--1402. Cited by 3 (0.71year) Abstract: Recently, support vector regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are non-stationary and noisy in nature. The volatility, usually time-varying, of the time series is therefore some valuable information about the series. Previously, we had proposed to use the volatility to adaptively change the width of the margin of SVR. We have noticed that upside margin and downside margin do not necessary be the same, and we have observed that their choice would affect the upside risk, downside risk and as well as the overall prediction result. In this paper, we introduce a novel approach to adapt the asymmetrical margins using momentum. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average. used SVM regression with a non-fixed and asymmetrical margin, this time adapting the asymmetrical margins using momentum, and applied it to predicting the Hang Seng Index and the Dow Jones Industrial Average. GAVRISHCHAKA, Valeriy V. and Supriya B. GANGULI, 2003. Volatility forecasting from multiscale and high-dimensional market data. Neurocomputing . Volume 55, Issues 1-2 (September 2003), Pages 285-305. Cited by 2 (0.63year) Abstract: Advantages and limitations of the existing volatility models for forecasting foreign-exchange and stock market volatility from multiscale and high-dimensional data have been identified. Support vector machines (SVM) have been proposed as a complimentary volatility model that is capable of effectively extracting information from multiscale and high-dimensional market data. SVM-based models can handle both long memory and multiscale effects of inhomogeneous markets without restrictive assumptions and approximations required by other models. Preliminary results with foreign-exchange data suggest that SVM can effectively work with high-dimensional inputs to account for volatility long-memory and multiscale effects. Advantages of the SVM-based models are expected to be of the utmost importance in the emerging field of high-frequency finance and in multivariate models for portfolio risk management. used SVMs for forecasting the volatility of foreign-exchange data. Their preliminary benchmark tests indicated that SVMs can perform significantly better than or comparable to both naive and GARCH(1,1) models. P201REZ-CRUZ, Fernando, Julio A. AFONSO-RODR205GUEZ and Javier GINER, 2003. Estimating GARCH models using support vector machines. Quantitative Finance . Volume 3, Number 3 (June 2003), Pages 163-172. Cited by 2 (0.63year) Abstract: Support vector machines (SVMs) are a new nonparametric tool for regression estimation. We will use this tool to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we will show that GARCH models can be estimated using SVMs and that such estimates have a higher predicting ability than those obtained via common ML methods. used SVMs for regression to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns and showed that such estimates have a higher predicting ability than those obtained via common maximum likelihood (ML) methods. Van GESTEL, T. et al. . 2003. Bankruptcy prediction with least squares support vector machine classifiers. In: 2003 IEEE International Conference on Computational Intelligence for Financial Engineering: Proceedings . pages 1-8. Cited by 2 (0.63year) Abstract: Classification algorithms like linear discriminant analysis and logistic regression are popular linear techniques for modelling and predicting corporate distress. These techniques aim at finding an optimal linear combination of explanatory input variables, such as, e. g. solvency and liquidity ratios, in order to analyse, model and predict corporate default risk. Recently, performant kernel based nonlinear classification techniques, like support vector machines, least squares support vector machines and kernel fisher discriminant analysis, have been developed. Basically, these methods map the inputs first in a nonlinear way to a high dimensional kernel-induced feature space, in which a linear classifier is constructed in the second step. Practical expressions are obtained in the so-called dual space by application of Mercers theorem. In this paper, we explain the relations between linear and nonlinear kernel based classification and illustrate their performance on predicting bankruptcy of mid-cap firms in Belgium and the Netherlands. used least squares support vector machine classifiers for predicting bankruptcy of mid-cap firms in Belgium and the Netherlands. CAO, L. J. and W. K. CHONG, 2002. Feature extraction in support vector machine: a comparison of PCA, XPCA and ICA. ICONIP 02: Proceedings of the 9th International Conference on Neural Information Processing, Volume 2 . edited by Lipo Wang, et al. . pages 1001-1005. Cited by 2 (0.48year) Abstract: Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, feature extraction is the first important step. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. PCA linearly transforms the original inputs into uncorrelated features. KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into statistically independent features. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, there is better generalization performance in KPCA and ICA feature extraction than PCA feature extraction. considered the application of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVMs for feature extraction. By examining the sunspot data and one real futures contract, they showed that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, they found that there is better generalization performance in KPCA and ICA feature extraction than PCA feature extraction. CAO, L. J. and Francis E. H. TAY, 2000. Feature Selection for Support Vector Machines in Financial Time Series Forecasting. In: Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents . edited by Kwong Sak Leung, Lai-Wan Chan and Helen Meng, pages 268-273. Cited by 3 (0.48year) Abstract: This paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for feature selection. The importance of feature is ranked by evaluating the sensitivity of the network output to the feature input in terms of the partial derivative. A systematic approach to remove irrelevant features based on the sensitivity is developed. Five futures contracts are examined in the experiment. Based on the Simulation results, it is shown that that saliency analysis is effective in SVMs for identifying important features. dealt with the application of saliency analysis to feature selection for SVMs. Five futures contracts were examined and they concluded that saliency analysis is effective in SVMs for identifying important features. ZHOU, Dianmin, Feng GAO and Xiaohong GUAN, 2004. Application of accurate online support vector regression in energy price forecast. WCICA 2004: Fifth World Congress on Intelligent Control and Automation, Volume 2 . pages 1838-1842. Cited by 1 (0.45year) Abstract: Energy price is the most important indicator in electricity markets and its characteristics are related to the market mechanism and the change versus the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability. In this paper, an accurate online support vector regression (AOSVR) method is applied to update the price forecasting model. Numerical testing results show that the method is effective in forecasting the prices of the electric-power markets. applied an accurate online support vector regression (AOSVR) to forecasting the prices of the electric-power markets, results showed that it was effective. FAN, A. and M. PALANISWAMI, 2001. Stock selection using support vector machines. IJCNN01: International Joint Conference on Neural Networks, Volume 3 . Pages 1793-1798. Cited by 2 (0.38year) Abstract: We used the support vector machines (SVM) in a classification approach to beat the market. Given the fundamental accounting and price information of stocks trading on the Australian Stock Exchange, we attempt to use SVM to identify stocks that are likely to outperform the market by having exceptional returns. The equally weighted portfolio formed by the stocks selected by SVM has a total return of 208 over a five years period, significantly outperformed the benchmark of 71. We also give a new perspective with a class sensitivity tradeoff, whereby the output of SVM is interpreted as a probability measure and ranked, such that the stocks selected can be fixed to the top 25used SVMs for classification for stock selection on the Australian Stock Exchange and significantly outperformed the benchmark. Van GESTEL, Tony, et al. . 2000. Volatility Tube Support Vector Machines. Neural Network World . vol. 10, number 1, pp. 287-297. Cited by 2 (0.32year) Abstract: In Support Vector Machines (SVM8217s), a non-linear model is estimated based on solving a Quadratic Programming (QP) problem. The quadratic cost function consists of a maximum likelihood cost term with constant variance and a regularization term. By specifying a difference inclusion on the noise variance model, the maximum likelihood term is adopted for the case of heteroskedastic noise, which arises in financial time series. The resulting Volatility Tube SVM8217s are applied on the 1-day ahead prediction of the DAX30 stock index. The influence of todays closing prices of the New York Stock Exchange on the prediction of tomorrow8217s DAX30 closing price is analyzed. developed the Volatility Tube SVM and applied it to 1-day ahead prediction of the DAX30 stock index, and significant positive out-of-sample results were obtained. CAO, Li Juan, Kok Seng CHUA and Lim Kian GUAN, 2003. Combining KPCA with support vector machine for time series forecasting. In: 2003 IEEE International Conference on Computational Intelligence for Financial Engineering . pages 325-329. Cited by 1 (0.31year) Abstract: Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first important step is feature extraction. This paper applies kernel principal component analysis (KPCA) to SVM for feature extraction. KPCA is a nonlinear PCA developed by using the kernel method. It firstly transforms the original inputs into a high dimensional feature space and then calculates PCA in the high dimensional feature space. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature forms much better than that extraction using KPCA per without feature extraction. In comparison with PCA, there is also superior performance in KPCA. applied kernel principal component analysis (KPCA) to SVM for feature extraction. The authors examined sunspot data and one real futures contract, and found such feature extraction enhanced performance and also that KPCA was superior to PCA. YANG, Haiqin, 2003. Margin Variations in Support Vector Regression for the Stock Market Prediction. Degree of Master of Philosophy Thesis, Department of Computer Science Engineering, The Chinese University of Hong Kong, June 2003. Cited by 1 (0.31year) Abstract: Support Vector Regression (SVR) has been applied successfully to financial time series prediction recently. In SVR, the 949-insensitive loss function is usually used to measure the empirical risk. The margin in this loss function is fixed and symmetrical. Typically, researchers have used methods such as crossvalidation or random selection to select a suitable 949 for that particular data set. In addition, financial time series are usually embedded with noise and the associated risk varies with time. Using a fixed and symmetrical margin may have more risk inducing bad results and may lack the ability to capture the information of stock market promptly. In order to improve the prediction accuracy and to consider reducing the downside risk, we extend the standard SVR by varying the margin. By varying the width of the margin, we can reflect the change of volatility in the financial data by controlling the symmetry of margins, we are able to reduce the downside risk. Therefore, we focus on the study of setting the width of the margin and also the study of its symmetry property. For setting the width of margin, the Momentum (also including asymmetrical margin control) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are considered. Experiments are performed on two indices: Hang Seng Index (HSI) and Dow Jones Industrial Average (DJIA) for the Momentum method and three indices: Nikkei225, DJIA and FTSE100, for GARCH models, respectively. The experimental results indicate that these methods improve the predictive performance comparing with the standard SVR and benchmark model. On the study of the symmetry property, we give a sufficient condition to prove that the predicted value is monotone decreasing to the increase of the up margin. Therefore, we can reduce the predictive downside risk, or keep it zero, by increasing the up margin. An algorithm is also proposed to test the validity of this condition, such that we may know the changing trend of predictive downside risk by only running this algorithm on the training data set without performing actual prediction procedure. Experimental results also validate our analysis. employs SVMs for regression and varys the width of the margin to reflect the change of volatility and controls the symmetry of margins to reduce the downside risk. Results were positive. CALVO, Rafael A. and Ken WILLIAMS, 2002. Automatic Categorization of Announcements on the Australian Stock Exchange. Cited by 1 (0.24year) Abstract: This paper compares the performance of several machine learning algorithms for the automatic categorization of corporate announcements in the Australian Stock Exchange (ASX) Signal G data stream. The article also describes some of the applications that the categorization of corporate announcements may enable. We have performed tests on two categorization tasks: market sensitivity, which indicates whether an announcement will have an impact on the market, and report type, which classifies each announcement into one of the report categories defined by the ASX. We have tried Neural Networks, a Na239ve Bayes classifier, and Support Vector Machines and achieved good resultspared the performance of neural networks, a na ve bayes classifier, and SVMs for the automatic categorization of corporate announcements in the Australian Stock Exchange (ASX) Signal G data stream. The results were all good, but with the SVM underperforming the other two models. AHMED, A. H.M. T. 2000. Forecasting of foreign exchange rate time series using support vector regression. 3rd year project. Computer Science Department, University of Manchester. Cited by 1 (0.16year)used support vector regression for forecasting a foreign exchange rate time series. GUESDE, Bazile, 2000. Predicting foreign exchange rates with support vector regression machines. MSc thesis. Computer Science Department, University of Manchester. Cited by 1 (0.16year) Abstract: This thesis investigates how Support Vector Regression can be applied to forecasting foreign exchange rates. At first we introduce the reader to this non linear kernel based regression and demonstrate how it can be used for time series prediction. Then we define a predictive framework and apply it to the Canadian exchange rates. But the non-stationarity in the data, which we here define as a drift in the map of the dynamics, forces us to present and use the typical learning processes for catching different dynamics. Our implementation of these solutions include Clusters of Volatility and competing experts. Finally those experts are used in a financial vote trading system and substantial profits are achieved. Through out the thesis we hope the reader will be intrigued by the results of our analysis and be encouraged in other dircetions for further research. used SVMs for regression to predict the Canadian exchange rate, wisely recognised the problem of nonstationarity, dealt with it using experts and claimed that substantial profits were achieved. BAO, Yu-Kun, et al. . 2005. Forecasting Stock Composite Index by Fuzzy Support Vector Machines Regression. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Volume 6 . pages 3535-3540. not cited (0year) Abstract: Financial time series forecasting methods such as exponential smoothing are commonly used for prediction on stock composition index (SCI) and have made great contribution in practice, but efforts on looking for superior forecasting method are still made by practitioners and academia. This paper deals with the application of a novel neural network technique, fuzzy support vector machines regression (FSVMR), in SCI forecasting. The objective of this paper is not only to examine the feasibility of FSVMR in SCI forecasting but presents our efforts on improving the accuracy of FSVMR in terms of data pre-processing, kernel function selection and parameters selection. A data set from Shanghai Stock Exchange is used for the experiment to test the validity of FSVMR. The experiment shows FSVMR a better method in SCI forecasting. used fuzzy support vector machines regression (FSVMR) to forecast a data set from the Shanghai Stock Exchange with positive results. CHEN, Kuan-Yu and Chia-Hui HO, 2005. An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting. ICNNB 05: International Conference on Neural Networks and Brain, 2005, Volume 3 not cited (0year) Abstract: This study applies a novel neural network technique, Support Vector Regression (SVR), to Taiwan Stock Exchange Market Weighted Index (TAIEX) forecasting. To build an effective SVR model, SVRs parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVRs optimal parameters using real value genetic algorithms. The experimental results demonstrate that SVR outperforms the ANN and RW models based on the Normalized Mean Square Error (NMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Moreover, in order to test the importance and understand the features of SVR model, this study examines the effects of the number of input node. used an SVM for regression for forecasting the Taiwan Stock Exchange Market Weighted Index (TAIEX). The results demonstrated that the SVR outperformed the ANN and RW models. CHEN, Wun-Hwa and Jen-Ying SHIH, 2006. A study of Taiwan39s issuer credit rating systems using support vector machines. Expert Systems with Applications . Volume 30, Issue 3, April 2006, Pages 427-435. not cited (0year) By providing credit risk information, credit rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries. In this paper, we propose an automatic classification model for issuer credit ratings, a type of fundamental credit rating information, by applying the support vector machine (SVM) method. This is a novel classification algorithm that is famous for dealing with high dimension classifications. We also use three new variables: stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification. Previous research has seldom considered these variables. The data period of the input variables used in this study covers three years, while most previous research has only considered one year. We compare our SVM model with the back propagation neural network (BP), a well-known credit rating classification method. Our experiment results show that the SVM classification model performs better than the BP model. The accuracy rate (84.62) is also higher than previous research. used an SVM to classify Taiwans issuer credit ratings and found that it performed better than the back propagation neural network (BP) model. CHEN, Wun-Hua, Jen-Ying SHIH and Soushan WU, 2006. Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. International Journal of Electronic Finance . Volume, Issue 1, pages 49-67. not cited (0year) Abstract: Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian markets. This research applies Support-Vector Machines (SVMs) and Back Propagation (BP) neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researchespared SVMs and back propagation (BP) neural networks when forecasting the six major Asian stock markets. Both models perform better than the benchmark AR (1) model in the deviation measurement criteria, whilst SVMs performed better than the BP model in four out of six markets. GAVRISHCHAKA, Valeriy V. and Supriya BANERJEE, 2006. Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting. Computational Management Science . Volume 3, Number 2 (April 2006), Pages 147-160. not cited (0year) Abstract: Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data. used SVMs for forecasting stock market volatility with positive results. HOVSEPIAN, K. and P. ANSELMO, 2005. Heuristic Solutions to Technical Issues Associated with Clustered Volatility Prediction using Support Vector Machines. ICNNampB3905: International Conference on Neural Networks and Brain, 2005, Volume 3 . Pages 1656-1660. not cited (0year) Abstract: We outline technological issues and our fimdings for the problem of prediction of relative volatility bursts in dynamic time-series utilizing support vector classifiers (SVC). The core approach used for prediction has been applied successfully to detection of relative volatility clusters. In applying it to prediction, the main issue is the selection of the SVC trainingtesting set. We describe three selection schemes and experimentally compare their performances in order to propose a method for training the SVC for the prediction problem. In addition to performing cross-validation experiments, we propose an improved variation to sliding window experiments utilizing the output from SVCs decision function. Together with these experiments, we show that accurate and robust prediction of volatile bursts can be achieved with our approach. used SVMs for classification to predict relative volatility clusters and achieved accurate and robust results. INCE, H. and T. B. TRAFALIS, 2004. Kernel principal component analysis and support vector machines for stock price prediction. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Volume 3 . pages 2053-2058. not cited (0year) Abstract: Financial time series are complex, non-stationary and deterministically chaotic. Technical indicators are used with principal component analysis (PCA) in order to identify the most influential inputs in the context of the forecasting model. Neural networks (NN) and support vector regression (SVR) are used with different inputs. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from technical analysis. Comparison shows that SVR and MLP networks require different inputs. The MLP networks outperform the SVR technique. found that MLP neural networks outperform support vector regression when applied to stock price prediction. KAMRUZZAMAN, Joarder, Ruhul A SARKER and Iftekhar AHMAD, 2003. SVM Based Models for Predicting Foreign Currency Exchange Rates. Proceedings of the Third IEEE International Conference on Data Mining (ICDM03) . Pages 557-560. not cited (0year) Abstract: Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e. g. neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and varepsilon - insensitive loss function. In this paper, we investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented. investigated the effect of different kernel functions and the regularization parameter when using SVMs to predict six different foreign currency exchange rates against the Australian dollar. investigated comprehensible credit scoring models using rule extraction from SVMs. NALBANTOV, Georgi, Rob BAUER and Ida SPRINKHUIZEN-KUYPER, 2006. Equity Style Timing Using Support Vector Regressions. to appear in Applied Financial Economics . not cited (0year) Abstract: The disappointing performance of value and small cap strategies shows that style consistency may not provide the long-term benefits often assumed in the literature. In this study we examine whether the short-term variation in the U. S. size and value premium is predictable. We document style-timing strategies based on technical and (macro-)economic predictors using a recently developed artificial intelligence tool called Support Vector Regressions (SVR). SVR are known for their ability to tackle the standard problem of overfitting, especially in multivariate settings. Our findings indicate that both premiums are predictable under fair levels of transaction costs and various forecasting horizons. used SVMs for regression for equity style timing with positive results. ONGSRITRAKUL, P. and N. SOONTHORNPHISAJ, 2003. Apply decision tree and support vector regression to predict the gold price. Proceedings of the International Joint Conference on Neural Networks, 2003, Volume 4 . Pages 2488-2492. not cited (0year) Abstract: Recently, support vector regression (SVR) was proposed to resolve time series prediction and regression problems. In this paper, we demonstrate the use of SVR techniques for predicting the cost of gold by using factors that have an effect on gold to estimate its price. We apply a decision tree algorithm for the feature selection task and then perform the regression process using forecasted indexes. Our experimental results show that the combination of the decision tree and SVR leads to a better performance. applied a decision tree algorithm for feature selection and then performed support vector regression to predict the gold price, their results were positive. Van GESTEL, Tony, et al. . 2005. Linear and non-linear credit scoring by combining logistic regression and support vector machines, Journal of Credit Risk . Vol. 1, No. 4, Fall 2005, Pages 31-60. not cited (0year) Abstract: The Basel II capital accord encourages banks to develop internal rating models that are financially intuitive, easily interpretable and optimally predictive for default. Standard linear logistic models are very easily readable but have limited model flexibility. Advanced neural network and support vector machine models (SVMs) are less straightforward to interpret but can capture more complex multivariate non-linear relations. A gradual approach that balances the interpretability and predictability requirements is applied here to rate banks. First, a linear model is estimated it is then improved by identifying univariate non-linear ratio transformations that emphasize distressed conditions and finally SVMs are added to capture remaining multivariate non-linear relations. apply linear and non-linear credit scoring by combining logistic regression and SVMs. YANG, Haiqin, et al. . 2004. Outliers Treatment in Support Vector Regression for Financial Time Series Prediction. Neural Information Processing: 11th International Conference, ICONIP 2004, Calcutta, India, November 2004, Proceedings not cited (0year) Abstract: Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting outliers and deflating their influence are important but hard problems. In this paper, we propose a novel 8220two-phase8221 SVR training algorithm to detect outliers and reduce their negative impact. Our experimental results on three indices: Hang Seng Index, NASDAQ, and FSTE 100 index show that the proposed 8220two-phase8221 algorithm has improvement on the prediction. proposed a novel two-phase SVR training procedure to detect and deflate the influence of outliers. The method was tested on the Hang Seng Index, NASDAQ and FSTE 100 index and results were positive. However, its not clear why the significance of outliers (such as market crashes) should be understated. YU, Lean, Shouyang WANG and Kin Keung LAI, 2005. Mining Stock Market Tendency Using GA-Based Support Vector Machines. Internet and Network Economics: First International Workshop, WINE 2005, Hong Kong, China, December 15-17, 2005, Proceedings (Lecture Notes in Computer Science) edited by Xiaotie Deng and Yinyu Ye, pages 336-345. not cited (0year) Abstract: In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on the historical data. To evaluate the forecasting ability of GASVM, we compare its performance with that of conventional methods (e. g. statistical models and time series models) and neural network models. The empirical results reveal that GASVM outperforms other forecasting models, implying that the proposed approach is a promising alternative to stock market tendency exploration. applied a random walk (RW) model, an autoregressive integrated moving average (ARIMA) model, an individual back-propagation neural network (BPNN) model, an individual SVM model and a genetic algorithm-based SVM (GASVM) to the task of predicting the direction of change in the daily SP500 stock price index and found that their proposed GASVM model performed the best. HARLAND, Zac, 2002. Using Support Vector Machines to Trade Aluminium on the LME.. Proceedings of the Ninth International Conference, Forecasting Financial Markets: Advances For Exchange Rates, Interest Rates and Asset Management . edited by C. Dunis and M. Dempster. not listed Abstract: This paper describes and evaluates the use of support vector regression to trade the three month Aluminium futures contract on the London Metal Exchange, over the period June 1987 to November 1999. The Support Vector Machine is a machine learning method for classification and regression and is fast replacing neural networks as the tool of choice for prediction and pattern recognition tasks, primarily due to their ability to generalise well on unseen data. The algorithm is founded on ideas derived from statistical learning theory and can be understood intuitively within a geometric framework. In this paper we use support vector regression to develop a number of trading submodels that when combined, result in a final model that exhibits above-average returns on out of sample data, thus providing some evidence that the aluminium futures price is less than efficient. Whether these inefficiencies will continue into the future is unknown. used an ensemble of SVMs for regression to trade the three month Aluminium futures contract on the London Metal Exchange with positive results. Van GESTEL, T. et al. . 2005. Credit rating systems by combining linear ordinal logistic regression and fixed-size least squares support vector machines, Workshop on Machine Learning in Finance, NIPS 2005 Conference, Whistler (British Columbia, Canada), Dec. 9.not listeddeveloped credit rating systems by combining linear ordinal logistic regression and fixed-size least squares SVMs. Forex predictions It is very difficult to predict how the market price of a currency will move in relation to another currency. Currency exchange rates are impacted by such as wide host of factors, including psychological ones and the intrinsic herd-mentality of speculative markets. Sometimes a simple rumor is enough to make a currency sink like a stone, at least temporary. Not only is it difficult to predict how the forex market will react to something, but it is also notoriously difficult to predict how strong that reaction will be, and what any counter-reactions will look like. This can make it hard to trade successfully with leveraged Forex certificates. A temporary reaction in the market can wipe out your position even if you are correct about the long term trend. It can often be better to use financial instrument such as binary options to benefit from trends on the currency market. Binary options give a good return and will not be affected by temporary drops in the market. The only thing that matters is the currency price at the time of maturity. Most Forex brokers do not offer trade with binary options. You will need a binary options broker account if you want to trade with binary options. Examples of factors that can influence the price of a currency in relation to other currencies The overall economic situation of the issuer of the currency. A strong economy will often mean a strong currency as well. Of course, if the currency becomes very highly valued, this can become problematic for export companies, and a problematic economic situation can arise for certain sectors of the country. At the same time, other sectors can be doing great since they profit from the low-cost of imported goods. The commercial balance of the issuer of the currency. A trade-deficiency will normally lead to a weakening of the currency. The political situation for the issuer of the currency. Unrest and instability will typically cause a drop in currency value. A stable political situation that is still not a good political situation can translate into a currency that is low value, but stable. Targeted speculation by one or several major currency traders. Sometimes even a comparatively small purchase or sale can be sufficient to trigger other traders to act in certain ways. It is often difficult to pin-point one specific reason for a currency to be weak or strong, or go up or down, since factors such as these tend to be intertwined with each other. The economic situation Different traders can also have different ideas about what actually constitutes good political and economic situation. There is for instance those who are very focused on Gross Domestic Product (GDP), while others prefer to also look at GDP at purchasing power parity per capita. Other important factors are national debt, retail sales and employmentunemploymentunderemployment statistics. One things that is very likely to cause a dramatic drop in currency value is an issuer that struggles to pay its debts. This will of course make the situation even worse for the issuer, if there are debts that must be paid in foreign currency. Commercial balance In the list above, the issuer8217s commercial balance is mentioned as one of the factors that can impact the market value of a currency. But what is this and how is it measured Commercial balance is the net export measured in local currency. If the issuer8217s (e. g. a country) exports are of a higher monetary value than the imports, the issuer has a positive commercial balance. If the value of the exports is smaller than the value of the imports, the commercial balance is negative. A negative commercial balance is also known as a trade deficit, and will typically bring the valuation of the currency down. Example: Country A exports a lot of high-value consumer goods. The countries that import all these products must pay for them using the currency of Country A. Therefore, the importing countries must purchase a lot of Country A currency at the forex market. The more sought after a currency is, the higher the price. The currency of Country A is therefore highly valued. If Country A had to import a lot of products, that could serve to bring the value of Country A currency down, since Country A would have to exchange a lot of its own currency for foreign currency at the forex market to pay for the imported goods. When Country A wants to sell a lot of its own currency, the availability of Country A currency at the forex market increases, and this impacts the demand-supply balance for Country A currency. It is important to remember that if the issuer is a country where producing goods for export is very important for the economy, the government might not want to see the currency get any stronger. A strong currency would make the exported products more expensive for foreign buyers, and the products might be out-competed by products produced in a country with a weaker currency. This would mean less revenue from exports, and probably also increased unemployment and underemployment as companies close down due to decreased foreign demand for their products. To avoid such a scenario, the government might take various actions in an effort to keep the currency from appreciating against other major currencies, and this is important for you to know if you are an FX trader. It should also be noted that a government might like the idea of having a low-valued currency since that can make domestically produced goods more sought after within the country, as imported goods becomes prohibitively expensive to purchase. Political stability and change When it comes to the forex market, political change can often have a larger impact than the overall political situation especially if we are looking at short-term fluctuations in exchange rates. This means that if something suddenly changes for the better for the issuer of a currency, the currency can appreciate markedly, even though the political situation is still very far from being good. The currency can appreciate in value against the currency of another country where the political situation is actually much better. The traders react to the change . Along the same lines, the valuation of a currency can drop sharply simply because a political situation is going from excellent to just fairly good. It should also be noted that sometimes a currency will appreciate simply as a reaction to the political situation in other countries. The political situation in Country A can be stable, but the currency is still going up like a rocket since the political situation in Country B, C and D is taking a turn for the worse and traders are rushing to own Country A currency. Of course, if Country A and Country B are neighbors or in any other way linked closely to each other, we might see the opposite thing happening. Country A is stable, but its currency is dropping in value anyway because traders fear that the political turmoil in Country B will soon impact Country A in a negative way. One of the reasons why fx traders shun political instability and social unrest is because they fear that investors (e. g. company owners) will pull out of the troublesome country or at the very least avoid making new investments. Such actions can lead to decreased demand for the currency, and traders don8217t want to find themselves stuck holding currency that few buyers want. Kommentarer är stängda. Find what you are looking for Avoid Scams Learn more about FXSVM Based Models for Predicting Foreign Currency Exchange Rates Concepts in SVM Based Models for Predicting Foreign Currency Exchange Rates Bureau de change A bureau de change or currency exchange is a business whose customers exchange one currency for another. Although originally French, the term bureau de change is widely used throughout Europe, and European travellers can usually easily identify these facilities when in other European countries. It is also common to find a sign saying Exchange or Change. Since the adoption of the euro, many exchange offices incorporate its logotype prominently on their signage. more from Wikipedia Foreign exchange market The foreign exchange market (forex, FX, or currency market) is a form of exchange for the global decentralized trading of international currencies. Finanscentra runt om i världen fungerar som ankare för handel mellan ett stort antal olika typer av köpare och säljare dygnet runt, med undantag för helger. Valutamarknaden bestämmer relativa värden för olika valutor. more from Wikipedia Support vector machine A support vector machine (SVM) is a concept in statistics and computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. The standard SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the input, making the SVM a non-probabilistic binary linear classifier. more from Wikipedia Exchange rate In finance, an exchange rate (also known as the foreign-exchange rate, forex rate or FX rate) between two currencies is the rate at which one currency will be exchanged for another. It is also regarded as the value of one countrys currency in terms of another currency. For example, an interbank exchange rate of 91 Japanese yen (JPY, ) to the United States dollar (US) means that 91 will be exchanged for each US1 or that US1 will be exchanged for each 91. more from Wikipedia Positive-definite kernel In operator theory, a branch of mathematics, a positive definite kernel is a generalization of a positive-definite matrix. more from Wikipedia Forecasting Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgemental methods. more from Wikipedia Regularization (mathematics) In mathematics and statistics, particularly in the fields of machine learning and inverse problems, regularization involves introducing additional information in order to solve an ill-posed problem or to prevent overfitting. This information is usually of the form of a penalty for complexity, such as restrictions for smoothness or bounds on the vector space norm. A theoretical justification for regularization is that it attempts to impose Occams razor on the solution. more from Wikipedia Tools and Resources Publisher Site Contact Us Switch to single page view (no tabs) Javascript is not enabled and is required for the tabbed view or switch to the single page view The ACM Digital Library is published by the Association for Computing Machinery. Copyright copy 2017 ACM, Inc. 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