Time series analysis and prediction using case based reasoning technology. Analiza i predviđanja toka vremenskih serija pomoću 'case-based reasoning' -tehnologije

This thesis describes one promising approach where a problem of timeseries analysis and prediction was solved by using Case Based Reasoning(CBR) technology. Foundations and main concepts of this technology aredescribed in detail. Furthermore, a detailed study of different approaches intime series analysis is given. System CuBaGe (Curve Base Generator) - Arobust and general architecture for curve representation and indexing timeseries databases, based on Case based reasoning technology, wasdeveloped. Also, a corresponding similarity measure was modelled for agiven kind of curve representation. The presented architecture may beemployed equally well not only in conventional time series (where allvalues are known), but also in some non-standard time series (sparse,vague, non-equidistant). Dealing with the non-standard time series is thehighest advantage of the presented architecture.
U ovoj doktorskoj disertaciji prikazan je interesantan i perspektivan pristuprešavanja problema analize i predviđanja vremenskih serija korišćenjemCase Based Reasoning (CBR) tehnologije. Detaljno su opisane osnove iglavni koncepti ove tehnologije. Takođe, data je komparativna analizarazličitih pristupa u analizi vremenskih serija sa posebnim osvrtom napredviđanje. Kao najveći doprinos ove disertacije, prikazan je sistemCuBaGe (Curve Base Generator) u kome je realizovan originalni načinreprezentacije vremenskih serija zajedno sa, takođe originalnom,odgovarajućom merom sličnosti. Robusnost i generalnost sistemailustrovana je realnom primenom u domenu finansijskog predviđanja, gdeje pokazano da sistem jednako dobro funkcioniše sa standardnim, ali i sanekim nestandardnim vremenskim serijama (neodređenim, retkim ineekvidistantnim).