Comparative performance of some time series forecasting models

A time series is a set of ordered observations on a quantitative characteristics of a phenomenon at equally spaced time point. One of the main goals of time series analysis is to forecast future values of the series. In time series analysis modeling, the prediction of values of future period is based on the pattern of past values of the variable under study. The present study aimed at comparing the performance of smoothing exponential methods with moving averages methods and arriving the best model decision by using some of the error measures like MAPE, MSE. From the current study, it is observed that double exponential smoothing method is comparatively better performs with other methods.