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

Sheetal S. Patil, S. H. Patil, A. M. Pawar


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.


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

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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:

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:

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.


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