Affiliations: [a] Department of Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic | [b] Centre ENET, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic | [c] Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Seri Iskandar, Malaysia
Correspondence:
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Corresponding author: Tomas Vantuch, Department of Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15 708 33, Ostrava-Poruba, Czech Republic. E-mail: tomas.vantuch@vsb.cz.
Abstract: The examination of the Elliott Wave theory is the main motivation of this contribution. All of the fundamental features of an proper Elliott Wave pattern (EW pattern) are reviewed and explained. Based on this knowledge, an algorithm for detection of these patterns is designed, developed and tested. Under several different algorithm settings, several EW pattern sets are obtained. They differ in amount of found EW patterns, quality and size. The following application of the developed detection algorithm was based on recognition of an incomplete EW patterns with aim of the prediction of the following progress of the time set. The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. The accuracy of trend prediction above 70% proves the relevancy of EW patterns on stock market data as well as the validity of the algorithm as a tool for detection of such patterns.
Keywords: Time series forecasting, Elliott waves, stock markets, support vector machine, random forest
