STUMPY, a powerful and scalable Python library for time series data mining, has been developed by Sean M. Law and others. This innovative tool enables users to efficiently analyze large datasets, making it an essential resource for researchers and scientists. The library builds upon the Matrix Profile algorithm, which was introduced in a series of research papers by Chin-Chia Michael Yeh, Yan Zhu, and other experts in the field. STUMPY is designed to be highly scalable, allowing it to handle massive datasets with ease. The development of this library has been supported by TD Ameritrade, a leading financial services company, which holds the trademark for STUMPY.
STUMPY can be used for:
- pattern/motif (approximately repeated subsequences within a longer time series) discovery
- anomaly/novelty (discord) discovery
- shapelet discovery
- semantic segmentation
- streaming (on-line) data
- fast approximate matrix profiles
- time series chains (temporally ordered set of subsequence patterns)
- snippets for summarizing long time series
- pan matrix profiles for selecting the best subsequence window size(s)
- and more …
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