Abstract

Abstract. The popularity of time-series databases in many applications has cre-ated 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 implemen-tations 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 multi-purpose, 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 similar-ity measures and nearest-neighbor classification. The correctness of the imple-mentation is verified through a battery of experiments which involve diverse time-series data sets from the UCR repository.