Time Series Similarity Search Based on Positive and Negative Query

Traditional time series similarity search, based on relevance feedback, combines initial, positive and negative relevant series directly to create new query sequence for the next search; it can’t make full use of the negative relevant sequence, even results in inaccurate query results due to excessive adjustment of the query sequence in some cases. In this paper, time series similarity search based on separate relevance feedback is proposed, each round of query includes positive query and negative query, and combines the results of them to generate the query results of each round. For one data sequence, positive query evaluates its similarity to the initial and positive relevant sequences, and negative query evaluates it’s similarity to the negative relevant sequences. The final similar sequences should be not only close to positive relevant series but also far away from negative relevant series. The experiments on UCR data sets showed that, compared with the retrieval method without feedback and the commonly used feedback algorithm the proposed method can improve accuracy of similarity search on some data sets.