AIM: Approximate Intelligent Matching for Time Series Data

Time-series data mining presents many challenges due to the intrinsic large scale and high dimensionality of the data sets. Subsequence similarity matching has been an active research area driven by the need to analyse large data sets in the financial, biomedical and scientific databases. In this paper, we investigate an intelligent subsequence similarity matching of time series queries based on efficient graph traversal. We introduce a new problem, the approximate partial matching of a query sequence in a time series database. Our system can address such queries with high specificity and minimal time and space overhead. The performance bottleneck of the current methods were analysed and we show our method can improve the performance of the time series queries significantly. It is general and flexible enough to find the best approximate match query without specifying a tolerance Ɛ parameter.