Publication Date


Document Type


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)


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


Department or Program

Department of Computer Science and Engineering

Year Degree Awarded