Open AccessPrediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model
Author(s) -
Salah L. Zubaidi,
Hussein Al-Bugharbee,
Khalid Hashim,
Nabeel Saleem Saad Al-Bdairi,
Sabeeh Lafta Farhan,
Asad Al Defae,
Mohammed J. Jameel
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/877/1/012031
Subject(s) - autoregressive model , logarithm , sample (material) , statistics , range (aeronautics) , maximum temperature , mathematics , econometrics , computer science , climatology , engineering , mathematical analysis , chemistry , chromatography , geology , aerospace engineering
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.