Applied Time Series and Duluth Temperature Prediction

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Applied Time Series and Duluth Temperature Prediction

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Autoregressive 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.


University of Minnesota M.S. thesis. June 2017. Major: Mathematics and Statistics. Advisor: Yongcheng Qi. 1 computer file (PDF); vii, 39 pages, tables

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Wan, Xiangpeng. (2017). Applied Time Series and Duluth Temperature Prediction. Retrieved from the University Digital Conservancy,

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