A Data-Driven Model Approach for DayWise Stock Prediction

Abstract

Economy of a country is closely related to stock market. By analysing stock market performance, we can evaluate whether a country’s economic growth is increasing or decreasing. Even though country’s economic growth can be understood by predicting stock market, it is highly unpredictable. We used dynamic mode decomposition which is a spatio-temporal, equation-free, data-driven algorithm for stock market prediction Schmid (J Fluid Mech 656:5–28, [13]) by considering stock markets as a dynamical system. How the system evolves and prediction of future state is done using DMD by decomposing a spatio-temporal system to modes having predetermined temporal behaviour. We used this property of DMD to predict stock market behaviour. In Kuttichira et al. (2017 international conference on advances in computing, communications and informatics (ICACCI), IEEE, pp 55–60, [7]) DMD was used to predict Indian stock market for minutewise data. We used daywise data as our timescale. Time series data of stock price of companies listed in National Stock Exchange were used as data. Sampled daywise stock price of companies across sector was used to predict the stock price for next few days. Predicted prices were compared with original prices and mean absolute percentage error was used to calculate the deviation for every companies. We analysed the stock price prediction for both intra- and intersector companies. We used dynamic mode decomposition to predict the stock price using historical data. We also did fine tuning of sampling windows to find out the best parameters for our data set.