Forecasting Financial Time Series with Support Vector Machin(5)

Fig.4.Thesegraphsshowsthedependenceofthepredictiononthesizeofthetimeslotusedeachtimeforpredictionandtraining:Theusedkernelfunctionsarefromlefttoright:DTW,LCSSglobal,andLCSSlocal.Theroundmarksinthediagramdenotetheresultswithatrainingsetof75periods,whereassquareandtriangularmarksshowtheresultsfor150and300periods.

ingdynamickernelfunctions,itispossibletouseawholerangeoftheprecedingseriesandanalyzeitasawholewiththeSVM’skernel.Aswecouldshow,thisapproachsigni cantlyincreasesthepredictionaccuracyandreliablyperformsbetterthanastandardnaiveforecast.

Forreal-worldexperimentsandapplicationsofthedevel-opedsystem,aninterfacetothetechnicalanalysissoftwareInvestox[46]wascreated(seeFig.5).Usingthisapplication,itbecomesnotonlypossibletoverifytheresultsonhistoricaldatausingavirtualbroker,butalsotoapplythesystemdirectlytocurrentdatainputsinaconstantlyevolvingmarketenvironment(seealso[47]).

Inourfutureresearch,theperformanceofthedevelopedsystemwillbeexaminedindifferenttradingconstellations.Contrarytotheworkonend-of-daydata,theperformanceofthetechniqueisalsohighenoughtouseitintheareaofintra-dayforecasting.Thisinvolvespredictionsinintervalsofonlyseveralminutes,ifnotjustinseconds’intervals.Inthisenvironmentofhighuncertaintyandconstanttrendshift,verydifferentrequirementsmayapply.Ontheotherhand,itisalsopossibletonotonlyusetheinputofonepre-processedtimeseries,buttocombinedifferentmarketpricesforpredictingacertainvalue.Thiskindofinter-marketanalysismayhavethepotentialtodetect uctuationsinaspeci cpriceandprematurelyratetheresultingin uenceonthetargetvalue.

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