Sparse and smooth adjustments for coherent forecasts in temporal aggregation of time series

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Abstract

Independent forecasts obtained from different temporal aggregates of a given time series may not be mutually consistent. State-of-the art forecasting methods usually apply adjustments on the individual level forecasts to satisfy the aggregation constraints. These adjustments require the estimation of the covariance between the individual forecast errors at all aggregation levels. In order to keep a maximum number of individual forecasts unaffected by estimation errors, we propose a new forecasting algorithm that provides sparse
and smooth adjustments while still preserving the aggregation constraints. The algorithm computes the revised forecasts by solving a generalized lasso problem. It is shown that it not only provides accurate forecasts, but also applies a significantly smaller number of adjustments to the base forecasts in a large-scale smart meter dataset.
Original languageEnglish
Title of host publicationNIPS 2016 Time Series Workshop, 09 December 2016, Barcelona, Spain
EditorsOren Anava, Azadeh Khaleghi, Marco Cuturi, Vitaly Kuznetsov, Alexander Rakhlin
Place of PublicationUSA
PublisherProceedings of Machine Learning Research (PMLR)
Number of pages11
Volume55
Publication statusPublished - 2016
EventNIPS Time Series Workshop 2016 - Centre Convencions Internacional Barcelona, Barcelona, Spain
Duration: 9 Dec 20169 Dec 2016
https://sites.google.com/site/nipsts2016/home

Workshop

WorkshopNIPS Time Series Workshop 2016
CountrySpain
CityBarcelona
Period9/12/169/12/16
Internet address

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