The similarity measurement of time series has important application value in multiple fields. Among them, the approximate representation and similarity measurement of sequences are the key to solving similarity search. The main purpose of this study is to solve the problem that traditional similarity measures cannot capture the similarity between long time series well. By using segmented local representation and the loss function based on local representation of time series, the loss distance is proposed as an indicator for similarity measurement. The method proposed in this paper is to match the traditional point-to-point according to the time scale to the local representation matching between the current segments according to the morphological characteristics of the time series, which saves the time cost and improves the efficiency of the model.

Jing Zhou, Mingwei Li. Research on Improving Time Series Similarity Based on Segmented Local Representation. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 4: 45-55. https://doi.org/10.25236/AJCIS.2024.070407.

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