Applied Time Series and Duluth Temperature Prediction

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Applied Time Series and Duluth Temperature Prediction

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Wan, Xiangpeng

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2017-06

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Abstract

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.

Keywords

Time series data

Duluth Minnesota

Autoregressive integrated moving average

ARIMA

Triple Exponential Smoothing

SARIMA

Swenson College of Science and Engineering

Department of Mathematics and Statistics

Master of Science

University of Minnesota Duluth

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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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Master's Degree Culminating Works
Master of Science in Mathematical Sciences and Master of Science in Applied and Computational Mathematics Plan B Project Papers

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Wan, Xiangpeng. (2017). Applied Time Series and Duluth Temperature Prediction. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/189098.

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