Towards a General Method for Logical Rule Extraction from Time Series
| Autor: | Alessandro Vaccari, Ionel Eduard Stan, Guido Sciavicco |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Time series
General method Series (mathematics) Computer science Rule extraction Timelines Timeline 0102 computer and information sciences 02 engineering and technology computer.software_genre 01 natural sciences NO 010201 computation theory & mathematics Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering A priori and a posteriori 020201 artificial intelligence & image processing Data mining Adaptation (computer science) Temporal data mining computer Abstraction (linguistics) |
| Zdroj: | From Bioinspired Systems and Biomedical Applications to Machine Learning ISBN: 9783030196509 IWINAC (2) |
| Popis: | Extracting rules from temporal series is a well-established temporal data mining technique. The current literature contains a number of different algorithms and experiments that allow one to abstract temporal series and, later, extract meaningful rules from them. In this paper, we approach this problem in a rather general way, without resorting, as many other methods, to expert knowledge and ad-hoc solutions. Our very simple temporal abstraction method allows us to transform time series into timelines, which can be then used for logical temporal rule extraction using an already existing temporal adaptation of the algorithm APRIORI. We have tested this approach on real data, obtaining promising results. |
| Databáze: | OpenAIRE |
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