Wan, Xiangpeng2017-07-252017-07-252017-06https://hdl.handle.net/11299/189098University of Minnesota M.S. thesis. June 2017. Major: Mathematics and Statistics. Advisor: Yongcheng Qi. 1 computer file (PDF); vii, 39 pages, tablesAutoregressive integrated moving average (ARIMA) has been one of the popular linear models in time series forecasting during the past three decades.The Triple Expo- nential Model also can be used to fit the time series data. This project takes Duluth temperature predictions as a case study, finding the best statistical model to predict the temperature. I collected 30 years of Duluth monthly maximum temperature data, from 1986 to 2016, and I fi t 29 years of them into di erent models including Triple Exponential Smoothing model, ARIMA model, and SARIMA model. Then I predicted the last year's temperature in those models, and I compared them to the true value of last year's temperature, which gave me the SSE value for each model so that I could find the best model.enTime series dataDuluth MinnesotaAutoregressive integrated moving averageARIMATriple Exponential SmoothingSARIMASwenson College of Science and EngineeringDepartment of Mathematics and StatisticsMaster of ScienceUniversity of Minnesota DuluthPlan Bs (project-based master's degrees)Master of Science in Applied and Computational MathematicsScholarly Text or Essay