Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation

Abstract

In this paper we are going to discuss the prediction of the financial time series using the Markov chain changing transition matrix model using genetic algorithm. During initial phase of the algorithm we will create the window of fix size with fixed number of state. The basic aim of this paper is to reduce the time taken to find the best window size and best number of states in the window by using the genetic algorithm. This paper produce the approach so that investor can save their time to predict the series without manual activity. To demonstrate the genetic algorithm optimisation we used the historical index data: national stock exchange(NSE50). The Nifty data contained 1239 candles starting from January 1,2015 and ending December 31, 2019. Data was downloaded from [ https://www1.nseindia.com/ ]. In this case we observed the better investment strategy using the first order Markov chain model and reducing the execution time by using the genetic algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.