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Segmentation of time series in up- and down-trends using the epsilon-tau procedure with application to USD/JPY foreign exchange market data.

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Commerce/*statistics & numerical data ; Financial Management/*statistics & numerical data ; Marketing/*statistics & numerical data; Internationality ; Japan ; Models, Statistical ; Probability ; Time Factors ; United States
    • Abstract:
      We propose the epsilon-tau procedure to determine up- and down-trends in a time series, working as a tool for its segmentation. The method denomination reflects the use of a tolerance level ε for the series values and a patience level τ in the time axis to delimit the trends. We first illustrate the procedure in discrete random walks, deriving the exact probability distributions of trend lengths and trend amplitudes, and then apply it to segment and analyze the trends of U.S. dollar (USD)/Japanese yen (JPY) market time series from 2015 to 2018. Besides studying the statistics of trend lengths and amplitudes, we investigate the internal structure of the trends by grouping trends with similar shapes and selecting clusters of shapes that rarely occur in the randomized data. Particularly, we identify a set of down-trends presenting similar sharp appreciation of the yen that are associated with exceptional events such as the Brexit Referendum in 2016.
      Competing Interests: The authors of this manuscript have the following competing interests: MT receives research funding (the Joint Collaborative Research Laboratory for MUFG AI Financial Market Analysis) from a commercial source (Mitsubishi UFJ financial group) as consultancy of AI trading strategies; HT is employed by Sony Computer Science Laboratories, Inc. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.
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    • Publication Date:
      Date Created: 20200918 Date Completed: 20201105 Latest Revision: 20201105
    • Publication Date:
      20250114
    • Accession Number:
      PMC7500655
    • Accession Number:
      10.1371/journal.pone.0239494
    • Accession Number:
      32946503