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Activity Number: 575
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #311947
Title: Forecasting Nonstationary Energy Time Series
Author(s): Marina Knight*+ and Rebecca Killick and Guy Nason and Idris Eckley
Companies: University of York and Lancaster University and University of Bristol and Lancaster University
Keywords: forecasting ; nonstationarity ; wavelets
Abstract:

Within the energy sector forecasting is an important statistical tool. Each day many forecasts are made across a variety of time scales, such as production of renewables, consumer demand and trader pricing. Traditional statistical techniques assume stationarity of the past in order to produce accurate forecasts. For data arising in the energy sector, this stationarity assumption is often violated- hence certain conditions need to be imposed on the time-varying structure of the process in order to achieve meaningful estimation. As many real-life data display characteristics from the locally stationary (LS) framework, we propose a new forecasting method for LS time series. Our approach hinges on introducing a new measure to characterise the behaviour of a LS wavelet process: the localised partial autocorrelation function. While the partial autocorrelation function is widely used in the stationary world, for nonstationary processes this is the first time when such a measure is defined, investigated and exploited. We discuss the theoretical properties of our estimators, illustrate our method with a simulation study and show improved forecasting errors using this new technique.


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