Comparative study on performance analysis of time series predictive models

Abstract

Time series models the analyses of data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time. Here five-time series datasets with different range of observation are considered to study its performance. In this paper, moving averages (MA) of series with different periods to average over are calculated; plotted series for forecasted data against original data; compared the performance of HOLT-WINTERS with the Auto Regressive Integrated Moving Average (ARIMA) model with non-zero mean; and computed the statistic test to examining the null hypothesis for the considered time series datasets.