Continuous Trend-Based Classification of Streaming Time Series

Trend analysis of time series data is an important research direction. In streaming time series the problem is more challenging, taking into account the fact that new values arrive for the series, probably in very high rates. Therefore, effective and efficient methods are required in order to classify a streaming time series based on its trend. Since new values are continuously arrive for each stream, the classification is performed by means of a sliding window which focuses on the last values of each stream. Each streaming time series is transformed to a vector by means of a Piecewise Linear Approximation (PLA) technique. The PLA vector is a sequence of symbols denoting the trend of the series, and it is constructed incrementally. The PLA is composed of a series of segments representing the trend of the raw data (either UP or DOWN). Efficient in-memory methods are used in order to: 1) determine the class of each streaming time series and 2) determine the streaming time series that comprise a specific trend class. Performance evaluation based on real-life datasets is performed, which shows the efficiency of the proposed approach both with respect to classification time and storage requirements. The proposed method can be used in order to continuously classify a set of streaming time series according to their trends, to monitor the behavior of a set of streams and to monitor the contents of a set of trend classes.