Fast approximate correlation for massive time-series data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Scaling-invariant boundary image matching using time-series matching techniques
Data & Knowledge Engineering
A review on time series data mining
Engineering Applications of Artificial Intelligence
Scalable kNN search on vertically stored time series
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Similarity-based time series retrieval has been a subject of long term study due to its wide usage in many applications, such as financial data analysis and weather data forecasting. Its original task was to find those time series similar to a pattern time series data, where both the pattern and data time series are static. Recently, with an increasing demand on stream data management, similarity-based stream time series retrieval has raised new research issues due to its unique requirements during the stream processing, such as one-pass search and fast response. In this paper, we address the problem of matching both static and dynamic patterns over stream time series data. We will develop a novel multi-scale representation, called multi-scale segment mean (MSM), for stream time series data, which can be incrementally computed and thus perfectly adapted to the stream characteristics. Most importantly, we propose a novel multi-step filtering mechanism, SS, over the multi-scale representation. Analysis indicates that the mechanism can greatly prune the search space and thus offer fast response. Furthermore, batching processing optimization, the dynamic case where patterns are also from stream time series, and pattern matching over future stream time series are also discussed. Extensive experiments show the proposed scheme can efficiently filter out false candidates and detect patterns.