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Time Series Representation Methods and Outliers Detection Techniques

In: The Baltic Dry Index

Author

Listed:
  • José Ramón San Cristóbal

    (University of Cantabria, Nautical School)

Abstract

Financial time series are usually high-dimensional, large in size and unstructured. The analysis of these time series can often be a complex and time-consuming task. Mining a time series is a useful approach to reduce its dimensionality while retaining the information associated with the relevant or important points in the time series. These points can be used for technical pattern matching, searching similarities, to discover hidden information or even to detect anomalies from either the original or the transformed time series.

Suggested Citation

  • José Ramón San Cristóbal, 2026. "Time Series Representation Methods and Outliers Detection Techniques," Contributions to Economics, in: The Baltic Dry Index, chapter 4, pages 37-48, Springer.
    • Handle: RePEc:spr:conchp:978-3-032-21073-9_4
    DOI: 10.1007/978-3-032-21073-9_4
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