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Ashton Williams Thesis.pdf (1.14 MB)

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Anomaly Detection in Multi-Seasonal Time Series Data

Williams, Ashton Taylor
http://rave.ohiolink.edu/etdc/view?acc_num=wright1685550289697458

Abstract Details

2023, Master of Science (MS), Wright State University, Computer Science.
Most of today’s time series data contain anomalies and multiple seasonalities, and accurate anomaly detection in these data is critical to almost any type of business. However, most mainstream forecasting models used for anomaly detection can only incorporate one or no seasonal component into their forecasts and cannot capture every known seasonal pattern in time series data. In this thesis, we propose a new multi-seasonal forecasting model for anomaly detection in time series data that extends the popular Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Our model, named multi-SARIMA, utilizes a time series dataset’s multiple pre-determined seasonal trends to increase anomaly detection accuracy even more than the original SARIMA model. Our experimental results demonstrate the higher accuracy of multi-SARIMA when multiple seasonalities are present than most models with one or no seasonal component, although with more processing time.
Soon M. Chung, Ph.D. (Advisor)
Vincent A. Schmidt, Ph.D. (Committee Member)
Nikolaos Bourbakis, Ph.D. (Committee Member)
44 p.
Computer Science; Information Science
anomaly detection; moving average; multiple seasonalities; multi-SARIMA; time series data; SARIMA

Recommended Citations

Citations

  • Williams, A. T. (2023). Anomaly Detection in Multi-Seasonal Time Series Data [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1685550289697458

    APA Style (7th edition)

  • Williams, Ashton. Anomaly Detection in Multi-Seasonal Time Series Data. 2023. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1685550289697458.

    MLA Style (8th edition)

  • Williams, Ashton. "Anomaly Detection in Multi-Seasonal Time Series Data." Master's thesis, Wright State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=wright1685550289697458

    Chicago Manual of Style (17th edition)

wright1685550289697458
347
© 2023, some rights reserved.Creative Commons License Anomaly Detection in Multi-Seasonal Time Series Data by Ashton Taylor Williams is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.