Open AccessOver-Differencing and Forecasting with Non-Stationary Time Series Data
Author(s) -
Zakir Hossain,
Ahmed S. Rahman,
Md. Moyazzem Hossain,
Jamil Hasan Karami
Publication year - 2019
Publication title -
the dhaka university journal of science
Language(s) - English
Resource type - Journals
eISSN - 2408-8528
pISSN - 1022-2502
DOI - 10.3329/dujs.v67i1.54568
Subject(s) - series (stratigraphy) , time series , transformation (genetics) , stationary process , order of integration (calculus) , computer science , mathematics , econometrics , algorithm , mathematical analysis , machine learning , geology , paleontology , biochemistry , chemistry , gene
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.
Dhaka Univ. J. Sci. 67(1): 21-26, 2019 (January)