Similarity-based Retrieval of Time-Series Data Using Multi-Scale Histograms

Queries for similarity-based time series data retrieval can be categorized into two types: (1) pattern existence queries, and (2) exact match queries. Pattern existence queries find the time series data with certain patterns while exact match queries search over the time series data by specifying exact values and detailed temporal information. In this paper, we propose multi-scale time series histograms that can be used to answer both types of queries, thus offering users more flexibility. The experimental results show that multi-scale histograms can effectively find the patterns in time series data as well as answer exact match queries, even when the data contain noise, local time shifting, local time scaling, amplitude shifting and amplitude scaling.

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