Abstract
Time series that arise on a graph or network arises in many scientific fields. In this paper we discuss a method for modelling and prediction of such time series with potentially complex characteristics. The method is based on the lifting scheme first proposed by Sweldens, a multiscale transformation suitable for irregular data with desirable properties. By repeated application of this algorithm we can transform the original network time series data into a simpler, lower dimensional time series object which is easier to forecast. The technique is illustrated with a data set arising from an energy time series application.
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Acknowledgements
We would like to thank the organisers of the “2nd Workshop on Industry Practices for Forecasting” (WIPFOR13) for an instructive and stimulating meeting. The authors would like to gratefully acknowledge the UK Met Office and British Atmospheric Data Centre (BADC) for access to the wind speed data.
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Nunes, M.A., Knight, M.I., Nason, G.P. (2015). Modelling and Prediction of Time Series Arising on a Graph. In: Antoniadis, A., Poggi, JM., Brossat, X. (eds) Modeling and Stochastic Learning for Forecasting in High Dimensions. Lecture Notes in Statistics(), vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-18732-7_10
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DOI: https://doi.org/10.1007/978-3-319-18732-7_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18731-0
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