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Spike It Up: Enhancing STL with Spike Detection for Intraday Volatility and Liquidity Forecasting

Published: 02 December 2024 Publication History

Abstract

RobustSTL is a recent seasonal-trend decomposition method to efficiently estimate, forecast, and detect anomalies for time series with complicated patterns. Our framework extends the RobustSTL method by incorporating spike detection and integrating these known regular spike occurrences with the traditional components. By modeling these known spikes, the enhanced STL approach significantly improves the accuracy of these complex time-series estimations and forecasting. Intraday volatility and liquidity in high-frequency trading data exhibit such complex patterns with trends, seasonality, outliers (jumps), spikes, and noise. Our empirical analysis demonstrates the improved efficiency of our method in forecasting applications, offering valuable implications for traders, risk analysts, and portfolio managers.

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    ICoMS '24: Proceedings of the 2024 7th International Conference on Mathematics and Statistics
    June 2024
    134 pages
    ISBN:9798400707223
    DOI:10.1145/3686592
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 02 December 2024

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    Author Tags

    1. Time series
    2. STL decomposition
    3. Forecasting
    4. Volatility

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