Author(s): Sheetal S. Patil, S.H. Patil

Email(s): sspatil@bvucoep.edu.in

DOI: 10.52711/2321-581X.2021.00017   

Address: Sheetal S. Patil*, S.H. Patil
Department of Computer Engineering, Bharati Vidyapeeth Deemed to be University College of Engineering Pune 411043, India.
*Corresponding Author

Published In:   Volume - 12,      Issue - 4,     Year - 2021


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


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