Study of Various Forecasting Models for Time Series Data Using Stochastic Processes

Authors

  • Sheetal S. Patil 1Assistant Professor, Department of Computer Engineering, Bharati Vidyapeeth Deemed to be University College of Engineering Pune, India
  • S. H. Patil 2Professor, Department of Computer Engineering, Bharati Vidyapeeth Deemed to be university College of Engineering Pune, India
  • A. M. Pawar 3Associate Professor, Department of Mechanical Engineering, Bharati Vidyapeeth's College of Engineering for Women, Pune, India

Keywords:

Time Series data, ARMA, ARIMA, SARIMA, Exponential Smoothing, Stochastic processes for forecasting

Abstract

The data which is in time stamped format is called as time series data. The time series data is everywhere, for example, weather data, stock market data, health care data, sensor data, network data, sales data and many more. Time series have various components due to which the time series data became complex. Trend, seasonality, cyclical, and irregularities, these are different components. As everyone is interested to know about future. That is why forecasting using time series data is important point of consideration. This research work focuses on components of time series data and simultaneously study of different time series modelling and forecasting techniques which are based on stochastic processes. Mainly, all the models discussed here focus on use of past time series data for forecasting future values. The research work covers AR, MA, Random Walk, ARMA, ARIMA, SARIMA, and Exponential Smoothing processes (single, double and triple) which are used for forecasting time series data.

References

Richard Lawton. Time Series Analysis and its Applications: Robert H. Shumway and David S. Stoffer; Springer Texts in Statistics; 2000. Int J Forecast. 2001; 17(2): 299–301.

Shumway Robert H, DSS. TimeSeries Analysis and Its Applications with R Examples. 3rd Edn. Springer; 2011.

Olatayo TO, Taiwo AI. Statistical Modelling and Prediction of Rainfall Time Series Data. Global Journal of Comuter Science and Technology (GJCST): G Interdisciplinary. 2014; 14(1): 1–10.

Etuk EH, Mohamed TM. Time Series Analysis of Monthly Rainfall data for the Gadaref rainfall station, Sudan, by Sarima Methods. Int J Sci Res Knowl. 2014 Jul; 320–327.

Kumar M, Thenmozhi M. Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. Int J Banking Account Financ. 2014; 5(3): 284–308.

Pal A, Prakash P. Practical Time Series Analysis. [Online]. Packt Publishing; 2017. Available: https://www.packtpub.com/big-data-and-business-intelligence/practical-time-series-analysis.

Narasanov Z. Time Series Forecasting Using a Moving Average Model for Extrapolation of Number of Tourist. UTMS J Econ. 2018; 9(2): 121–132.

Sadigov T. (2019). Practical Time Series Analysis. [Online]. Available: https://www.coursera.org/learn/practical-time-series-analysis.

Vijh M, Chandola D, Tikkiwal VA, Kumar A. Stock Closing Price Prediction using Machine Learning Techniques. Procedia Comput Sci. 2020; 167(2019): 599–606.

Viswanatha Reddy C. Predicting the Stock Market Index using Stochastic Time Series ARIMA Modelling: The Sample of BSE and NSE. Indian Journal of Finance. 2019;13(8):7-25.

Dhyani B, Kumar M, Verma P, Jain A. Stock Market Forecasting Technique using Arima Model. Int J Recent Technol Eng. 2020; 8(6): 2694–2697.

Andreea-cristina Petric AT, Stelian STANCU. Limitation of ARIMA models in financial and monetary economics. Theor Appl Econ. 2016; 23(4): 19–42.

Downloads

Published

2021-11-12

Issue

Vol. 12 No. 2 (2021)

Section

Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.