Over-Differencing and Forecasting with Non-Stationary Time Series Data

Abstract

In time series analysis, over-differencing is a common phenomenon to make the data to be stationary. However, it is not
always a good idea to take over-differencing in order to ensure the stationarity of time series data. In this paper, the effect of
over-differencing has been investigated via a simulation study to observe how far or how close the fitted model from the
true one. Simulation results show that the fitted model is found to be different and very far from the true model because of
over-differencing in most of the scenarios considered in this study. In practice, it may be worthy to consider differencing as
well as suitable transformation of the time series data to make it stationary. Both transformation and differencing are used
for a non-stationary time series data on average monthly house prices to ensure it to be stationary. We then analyse the data
and make prediction for the future values.