NDLI: Multivariate k-nearest neighbour regression for time series data — A novel algorithm for forecasting UK electricity demand

Content Provider	IEEE Xplore Digital Library
Author	Al-Qahtani, F.H. Crone, S.F.
Copyright Year	2013
Description	Author affiliation: Manage. Sch., Lancaster Centre for Forecasting, Lancaster Univ., Lancaster, UK (Al-Qahtani, F.H.; Crone, S.F.)
Abstract	The k-nearest neighbour (k-NN) algorithm is one of the most widely used benchmark algorithms in classification, supported by its simplicity and intuitiveness in finding similar instances in multivariate and large-dimensional feature spaces of arbitrary attribute scales. In contrast, only few scientific studies of k-NN exist in forecasting time series data, which have mainly assessed various distance metrics to identify similar univariate time series motifs in past data. In electricity load forecasting, k-NN studies are limited to identifying past motifs of the same dependent variable to match future realisations, in a non-causal approach to forecasting. However, causal information in the form of deterministic calendar information is readily available on past and future time series motifs, allowing the distinction between load profiles of working days, weekends and bank-holidays to be encoded as binary dummy variables, and to be efficiently included in the search for similar neighbours. In this paper, we propose a multivariate k-NN regression method for forecasting the electricity demand in the UK market which utilises binary dummy variables as a second feature to categorise the day being forecasted as a working day or a non-working day. We assess the efficacy of this approach in a robust empirical evaluation using UK electricity load data. The approach shows improvements beyond conventional k-NN approaches and accuracy beyond that of simple statistical benchmark methods.
Starting Page	1
Ending Page	8
File Size	461219
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6706742
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Forecasting Electricity Vectors Load modeling Predictive models Classification algorithms
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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