Rotation-invariant similarity in time series using bag-of-patterns representation

• DOI: 10.1007/s10844-012-0196-5
• Publication Date: 2012-03-31T11:16:51Z
• Authors: Jessica Lin; Rohan Khade; Yuan Li
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

For more than a decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search relies on shape-based similarity matching. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We performed extensive experiments and show that our approach outperforms the leading existing methods in clustering, classification, and anomaly detection on dozens of real datasets. We further demonstrate that the representation allows rotation-invariant matching in shape datasets.