Short-term Time Series Forecasting with Regression Automata

Reference : Short-term Time Series Forecasting with Regression Automata


	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/28623

Title :	Short-term Time Series Forecasting with Regression Automata
Language :	English
Author, co-author :	Lin, Qin [> >]
	Hammerschmidt, Christian [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
	Pellegrino, Gaetano [> >]
	Verwer, Sicco [> >]
Publication date :	2016
Peer reviewed :	Yes
Event name :	ACM SIGKDD 2016 Workshop on Mining and Learning from Time Series (MiLeTS)
Event date :	Aug 14, 2016
Keywords :	[en] regression ; automaton ; wind speed
Abstract :	[en] We present regression automata (RA), which are novel type syntactic models for time series forecasting. Building on top of conventional state-merging algorithms for identifying automata, RA use numeric data in addition to symbolic values and make predictions based on this data in a regression fashion. We apply our model to the problem of hourly wind speed and wind power forecasting. Our results show that RA outperform other state-of-the-art approaches for predicting both wind speed and power generation. In both cases, short-term predictions are used for resource allocation and infrastructure load balancing. For those critical tasks, the ability to inspect and interpret the generative model RA provide is an additional benefit.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/28623

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