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Scotty: Fast a priori Structure-based Extraction from Time Series

The recognition and extraction of data-driven pat- terns is a challenging task. In vast amounts of data, suitable techniques should prepare data that match the user’s intentions. For example, "an increasing daily price of a stock, is generally followed by a sudden fall due to the endorsed profit gains". Such a statement should effortlessly be transferred as input to a data mining task, without the need for an extensive training phase building an extremely complicated model and the usage of threshold-based approaches. We propose a method for extracting lexical representations directly from the raw data, enabling others not directly compatible with real-valued data. We provide evidence that our method is fast and accurate. We use as evaluation a preliminary step for a classification task compared to state-of-the-art classifiers applying it on a publicly available dataset.