DCRL: Approach for Pattern Recognition in Price Time Series using Directional Change and Reinforcement Learning

Developing an intelligent pattern recognition model for electronic markets has been a vital research direction in the field. Ongoing continues learning algorithms capable of recognizing and classifying price patterns hence providing investors market analysts with better insights into time-series. In this paper, adaptive Directional Change (DC) Reinforcement Learning (RL) is proposed, so called DCRL model. Compared traditional analytical approaches that uses fixed time interval specified features market, alternative approach samples time-series using event-based RL. model, environment’s behavior incorporated RL process to automate identification directional changes. The learns representation by adaptively selecting different depending on current state. evaluated Saudi stock data trends. A series analyses demonstrate effective performance detecting changes extensive applicability