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

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.