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

Gurdeep Saini; Naveen Yadav; Biju R. Mohan; Nagaraj Naik

doi:10.1007/978-981-15-9829-6_34

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

Gurdeep Saini^*
, Naveen Yadav
, Biju R. Mohan
, Nagaraj Naik

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Modeling, Simulation and Optimization - Proceedings of CoMSO 2020
Editors	Biplab Das, Ripon Patgiri, Sivaji Bandyopadhyay, Valentina Emilia Balas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	439-451
Number of pages	13
ISBN (Print)	9789811598289
DOIs	https://doi.org/10.1007/978-981-15-9829-6_34
Publication status	Published - 2021
Event	International Conference on Modeling, Simulation and Optimization, CoMSO 2020 - Silchar, India Duration: 03-08-2020 → 05-08-2020

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	206
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	International Conference on Modeling, Simulation and Optimization, CoMSO 2020
Country/Territory	India
City	Silchar
Period	03-08-20 → 05-08-20

All Science Journal Classification (ASJC) codes

General Decision Sciences
General Computer Science

Access to Document

10.1007/978-981-15-9829-6_34

Cite this

Saini, G., Yadav, N., Mohan, B. R., & Naik, N. (2021). Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation. In B. Das, R. Patgiri, S. Bandyopadhyay, & V. E. Balas (Eds.), Modeling, Simulation and Optimization - Proceedings of CoMSO 2020 (pp. 439-451). (Smart Innovation, Systems and Technologies; Vol. 206). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-9829-6_34

Saini, Gurdeep ; Yadav, Naveen ; Mohan, Biju R. et al. / Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation. Modeling, Simulation and Optimization - Proceedings of CoMSO 2020. editor / Biplab Das ; Ripon Patgiri ; Sivaji Bandyopadhyay ; Valentina Emilia Balas. Springer Science and Business Media Deutschland GmbH, 2021. pp. 439-451 (Smart Innovation, Systems and Technologies).

@inproceedings{d556595ea96647a0a4948fc8e5583859,

title = "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.",

author = "Gurdeep Saini and Naveen Yadav and Mohan, \{Biju R.\} and Nagaraj Naik",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Modeling, Simulation and Optimization, CoMSO 2020 ; Conference date: 03-08-2020 Through 05-08-2020",

year = "2021",

doi = "10.1007/978-981-15-9829-6\_34",

language = "English",

isbn = "9789811598289",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "439--451",

editor = "Biplab Das and Ripon Patgiri and Sivaji Bandyopadhyay and Balas, \{Valentina Emilia\}",

booktitle = "Modeling, Simulation and Optimization - Proceedings of CoMSO 2020",

address = "Germany",

}

Saini, G, Yadav, N, Mohan, BR & Naik, N 2021, Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation. in B Das, R Patgiri, S Bandyopadhyay & VE Balas (eds), Modeling, Simulation and Optimization - Proceedings of CoMSO 2020. Smart Innovation, Systems and Technologies, vol. 206, Springer Science and Business Media Deutschland GmbH, pp. 439-451, International Conference on Modeling, Simulation and Optimization, CoMSO 2020, Silchar, India, 03-08-20. https://doi.org/10.1007/978-981-15-9829-6_34

Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation. / Saini, Gurdeep; Yadav, Naveen; Mohan, Biju R. et al.
Modeling, Simulation and Optimization - Proceedings of CoMSO 2020. ed. / Biplab Das; Ripon Patgiri; Sivaji Bandyopadhyay; Valentina Emilia Balas. Springer Science and Business Media Deutschland GmbH, 2021. p. 439-451 (Smart Innovation, Systems and Technologies; Vol. 206).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation

AU - Saini, Gurdeep

AU - Yadav, Naveen

AU - Mohan, Biju R.

AU - Naik, Nagaraj

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

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Saini G, Yadav N, Mohan BR, Naik N. Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation. In Das B, Patgiri R, Bandyopadhyay S, Balas VE, editors, Modeling, Simulation and Optimization - Proceedings of CoMSO 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 439-451. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-15-9829-6_34

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

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