Time‐series forecasting using fractional differencing

Andrew Sutcliffe

45 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Time Series Analysis and Forecasting Statistical and numerical algorithms

Abstract

Abstract The main failure of ARIMA modelling as used in practice are the limiting constraints imposed by differencing to achieve stationarity. The use of fractional differencing opens up a much wider and realistic behaviour for the trend and seasonal components than traditional integer differencing. This paper shows several advantages of using fractional differencing for forecasting monthly data. These advantages are illustrated using a sample of previously modelled time‐series data selected from the literature.

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1977Time Series Analysis: Forecasting and Control 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 1983Statistical Methods for Forecasting 1984Statistical Methods for Forecasting.1983Forecasting Economic Time Series With Structural and Box-Jenkins Models: A Case Study 1983Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study 2020On the Power of Dickey-Fuller Tests against Fractional Alternatives

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