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

Salah L Zubaidi,Nabeel Saleem Saad Al-Bdairi,Khalid Hashim,Asad Al Defae,Mohammed J Jameel,Hussein Al-Bugharbee,Sabeeh L Farhan

doi:10.1088/1755-1315/877/1/012031

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.

Highlights

Climate change and climate variability are anticipated to cause significant issues for the ecosystem [1, 2]
Various studies showed that urban water consumption was driven by maximum temperature [24,25,26,27,28]
These models of fitness are obtained in different model order

Summary

Introduction

Climate change and climate variability are anticipated to cause significant issues for the ecosystem (i.e., increase the temperature in future) [1, 2]. Industrialisation and the massive use of fossil fuels caused an increase the greenhouse gases that led to an increase in the impact of climate change [3, 4]. It has located a substantial impact on the environment of residential area in various places of the world [5,6,7]. These influences differ concerning the region, the type, and the importance. Various studies showed that urban water consumption was driven by maximum temperature [24,25,26,27,28]

Methods

Results

Conclusion