CBR algorithm supporting time series data
基于卷积的时态CBR快速检索算法及应用*

This paper focused on the retrieval algorithms of a special kind of CBR system in which cases were composed of time-series data. Introduced the classical algorithm used for processing similarity queries on time series data, This algorithm was based on the fact that DFT preserved the Euclidean distance in the time or frequency domain, and only the first few elements of the frequency sequence were significant, so the retrieval process could only use these significant elements to compute similarity degree. However, this algorithm had several disadvantages limiting its usage in CBR retrieval, so developed a new algorithm using batch method to compute the similarity degree. It was based on the observation that the original problem could be transformed to a convolution problem, and the circular convolution could be computed more efficiently using FFT. Theoretical analysis and experiment results prove that this algorithm is efficient and robust. The presented algorithm furnished the CBR with the ability to process cases consist of time-series data, developed a time series prediction algorithm based on CBR and the experiment results proved its efficiency.