Reference : Short-term Time Series Forecasting with Regression Automata
Scientific congresses, symposiums and conference proceedings : Poster
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/28623
Short-term Time Series Forecasting with Regression Automata
English
Lin, Qin mailto [> >]
Hammerschmidt, Christian mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Pellegrino, Gaetano mailto [> >]
Verwer, Sicco mailto [> >]
2016
Yes
ACM SIGKDD 2016 Workshop on Mining and Learning from Time Series (MiLeTS)
Aug 14, 2016
[en] regression ; automaton ; wind speed
[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.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/28623

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