Open AccessPattern recognition using hidden Markov models in financial time series
Author(s) -
Sara Rebagliati,
Emanuela Sasso
Publication year - 2017
Publication title -
acta et commentationes universitatis tartuensis de mathematica./acta et commentationes universitatis tartuensis de mathematica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.276
H-Index - 6
eISSN - 2228-4699
pISSN - 1406-2283
DOI - 10.12697/acutm.2017.21.02
Subject(s) - hidden markov model , computer science , series (stratigraphy) , artificial intelligence , pattern recognition (psychology) , forward algorithm , markov model , time series , markov chain , machine learning , algorithm , finance , variable order markov model , economics , paleontology , biology
Our aim consists in developing a software which can recognize M trading patterns in real time using Hidden Markov Models (HMMs). A trading pattern is a predefined figure indicating a specific behavior of prices. We trained M + 1 HMMs using Baum-Welch Algorithm combined with Genetic Algorithm. In particular, with HMMs we describe M trading patterns while the other one, called threshold model, can recognize all the not predefined patterns. The classification algorithm correctly recognizes 93% of the provided patterns. Thanks to the analysis of the false positive examples, we finally designed some more filters to reduce them.