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 interested to know about future. That’s why Forecasting using time series data is important point of consideration. This research paper focuses on components of time series data 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 paper covers AR, MA, Random Walk, ARMA, ARIMA, SARIMA, and Exponential Smoothing processes (single, double and triple) which are used for forecasting time series data.
Cite this article:
Sheetal S. Patil, S.H. Patil. Study of Various Forecasting Models for time series data, using Stochastic Processes. Research Journal of Engineering and Technology. 2021;12(4):99-4. doi: 10.52711/2321-581X.2021.00017
Cite(Electronic):
Sheetal S. Patil, S.H. Patil. Study of Various Forecasting Models for time series data, using Stochastic Processes. Research Journal of Engineering and Technology. 2021;12(4):99-4. doi: 10.52711/2321-581X.2021.00017 Available on: https://ijersonline.org/AbstractView.aspx?PID=2021-12-4-2
REFERENCES:
1. Lawton, R. Time Series Analysis and its Applications. Int. J. Forecast. 2001; 17: 299–301.
2. Robert H. Shumway, D. S. S. (2016). TimeSeries Analysis and Its Applications with R Examples. o Title.
3. T.O.Olatayo and Taiwo, A. I. Statistical Modelling and Prediction of Rainfall Time Series Data. Glob. J. Comuter Sci. Technol. 2014; 14: 1–10.
4. Etuk, E. H. and Mohamed, T. M. Time Series Analysis of Monthly Rainfall data for the Gadaref rainfall station, Sudan, by Sarima Methods. Int. J. Sci. Res. Knowl. 2014; 320–327. doi:10.12983/ijsrk-2014-p0320-0327
5. Kumar*, V. Time series modeling and forecasting using stochastic models: A review. Int. J. Eng. Sci. Res. Technol. doi:: 10.5281/zenodo.205828
6. Sadigov and Thistleton. Practical Time Series Analysis. https://www.coursera.org Available at: https://www.coursera.org/learn/practical-time-series-analysis.
7. Narasanov, Z. Time Series Forecasting Using a Moving Average Model for Extrapolation of Number of Tourist. UTMS J. Econ. 2018; 9: 121–132.
8. Vijh, M., Chandola, D., Tikkiwal, V. A. and Kumar, A. Stock Closing Price Prediction using Machine Learning Techniques. Procedia Comput. Sci. 2020; 67: 599–606.
9. Dr. C. Viswanatha Reddy. Predicting the Stock Market Index Using Stochastic Time Series. 2018,
10. Dhyani, B., Kumar, M., Verma, P. and Jain, A. Stock Market Forecasting Technique using Arima Model. Int. J. Recent Technol. Eng. 2020; 8: 2694–2697.
11. Andreea-cristina Petric, Stelian STANCU, A. T. Limitation of ARIMA models in financial and monetary economics. Theor. Appl. Econ. 2016; 23: 19–42.