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A time series is a collection of measurements in chronological order.Discovering patterns from time series is useful in many domains, such as stockanalysis, disease detection, and weather forecast. To discover patterns,existing methods often convert time series data into another form, such asnominal/symbolic format, to reduce dimensionality, which inevitably deviatesthe data values. Moreover, existing methods mainly neglect the orderrelationships between time series values. To tackle these issues, inspired byorder-preserving matching, this paper proposes an Order-Preserving sequentialPattern (OPP) mining method, which represents patterns based on the orderrelationships of the time series data. An inherent advantage of suchrepresentation is that the trend of a time series can be represented by therelative order of the values underneath the time series data. To obtainfrequent trends in time series, we propose the OPP-Miner algorithm to minepatterns with the same trend (sub-sequences with the same relative order).OPP-Miner employs the filtration and verification strategies to calculate thesupport and uses pattern fusion strategy to generate candidate patterns. Tocompress the result set, we also study finding the maximal OPPs. Experimentsvalidate that OPP-Miner is not only efficient and scalable but can alsodiscover similar sub-sequences in time series. In addition, case studies showthat our algorithms have high utility in analyzing the COVID-19 epidemic byidentifying critical trends and improve the clustering performance.

OPP-Miner: Order-preserving sequential pattern mining