Abstract
The popularity of time-series databases in many applications has created an increasing demand for performing data-mining tasks (classification, clustering, outlier detection, etc.) on time-series data. Currently, however, no single system or library exists that specializes on providing efficient implementations of data-mining techniques for time-series data, supports the necessary concepts of representations, similarity measures and preprocessing tasks, and is at the same time freely available. For these reasons we have designed a multipurpose, multifunctional, extendable system FAP – Framework for Analysis and Prediction, which supports the aforementioned concepts and techniques for mining time-series data. This paper describes the architecture of FAP and the current version of its Java implementation which focuses on time-series similarity measures and nearest-neighbor classification. The correctness of the implementation is verified through a battery of experiments which involve diverse time-series data sets from the UCR repository.
This work was supported by project Abstract Methods and Applications in Computer Science (no. 144017A), of the Serbian Ministry of Science and Environmental Protection.
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Kurbalija, V., Radovanović, M., Geler, Z., Ivanović, M. (2010). A Framework for Time-Series Analysis. In: Dicheva, D., Dochev, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2010. Lecture Notes in Computer Science(), vol 6304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15431-7_5
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