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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. © 2010 Springer-Verlag.