Modeling and Forecasting Daily Temperature Time Series in the Memphis, Tennessee

Abstract

Temperature is an essential weather component because of its tremendous impact on humans and the environment. As a result, one of the widely researched parts of global climate change study is temperature forecasting. This work analyzes trends and forecasts a temperature change to see the transient variations over time using daily temperature data from January 1, 2016 – November 3, 2019, collected from a weather station located at the Memphis International Airport. The Mann-Kendall (M-K) test is used to detect time series analysis patterns as a non-parametric technique. The result from the test revealed that the temperature time series increased by 0.0030 °F almost every day, implying that the location is becoming hotter. The other method of analysis is the autoregressive integrated moving average (ARIMA) model, which fits temperature time series using its three standard processes of identification, diagnosis, and forecasting. Considering the selection criteria, The seasonal autoregressive integrated moving average (SARIMA) (3, 0, 0) (0, 1, 0)₃₆₅ model is found as appropriate for the studied temperature data on a daily basis. Finally, the selected model is utilized to estimate the next 50 days; after November 3, 2019, the temperature forecast showed an increasing trend. This observed trend provides an understanding of daily temperature change in the studied area for that specific period.