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Mixture Models for Prediction from Time Series, with Application to Energy Use Data

Qarmalah, Najla M.; Einbeck, Jochen; Coolen, Frank P. A.

Abstract:
This paper aims to use mixture models to produce predictions from time series data. Given data of the form (ti, yi), i = 1, . . . , T , we propose a mix- ture model localized at time point tT with the k-th component as yi = mk (ti) + εik with mixing proportions πk (ti) such that 0 ≤ πk (ti) ≤ 1 and ∑K πk (ti) = 1, where K is the number of components. The k (·) are smooth unspecified regression functions, and the errors εik ∼ N(0, σ 2) are independently distributed. Estimation of this model is achieved through a kernel-weighted version of the EM-algorithm, using exponential kernels with different bandwidths (neighbour- hood sizes) hk as weight functions. By modelling a mixture of local regressions at a target time point tT but with different bandwidths hk , the estimated mixture probabilities are informative for the amount of information available in the data set at the scale of resolution corresponding to each bandwidth. Nadaraya- Watson and local linear estimators are used to carry out the localized estimation step. For prediction at time point tT +1, adequate methods are provided for each local method, and compared to competi ... mehr

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Volltext §
DOI: 10.5445/KSP/1000058749/07
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 2363-9881
urn:nbn:de:swb:90-670192
KITopen-ID: 1000067019
Erschienen in Archives of Data Science, Series A (Online First)
Band 2
Heft 1
Seiten 15 S. online
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