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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.

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Time Series Representation Methods and Outliers Detection Techniques

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  • 发表日期：

    2026年

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  • José Ramón San Cristóbal

摘要

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.