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Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

Zubaidi, S, Al-Bugharbee, H, Hashim, KS, Al-Bdairi, N, Farhan, S, Al Defae, A and Jameel, M (2021) Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model. In: IOP Conference Series : Earth and Environmental Science . (IOP Conference Series: Earth and Environmental Science,, 1st-2nd September 2021, Erbil, Iraq).

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Abstract

This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model.

Item Type: Conference or Workshop Item (Paper)
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Engineering
Publisher: IOP Publishing
Date Deposited: 17 Nov 2021 12:09
Last Modified: 17 Nov 2021 12:15
DOI or Identification number: 10.1088/1755-1315/877/1/012031
URI: https://researchonline.ljmu.ac.uk/id/eprint/15795

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