Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model. Issue 1 (November 2021)

• Record Type: Journal Article
• Authors: Zubaidi, Salah L.; Al-Bugharbee, Hussein; Hashim, Khalid; Al-Bdairi, Nabeel Saleem Saad; Farhan, Sabeeh L.; Defae, Asad Al; Jameel, Mohammed J.
• Is Part Of: IOP conference series. Volume 877:Issue 1(2021)
• Publication Date: 2021-11
• Subjects: Autoregressive Model -- Baghdad City -- prediction model -- temperature; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/877/1/012031
• Languages: English
• ISSNs: 1755-1307

Abstract

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.