Publication Date
2023
Document Type
Thesis
Committee Members
Soon M. Chung, Ph.D. (Advisor); Vincent A. Schmidt, Ph.D. (Committee Member); Nikolaos Bourbakis, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
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.
Page Count
44
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2023
Copyright
Copyright 2023, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
