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 ![]() | |
Hammerschmidt, Christian ![]() | |
Pellegrino, Gaetano ![]() | |
Verwer, Sicco ![]() | |
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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