Forecasting of Monthly Rainfall in Dir (L) KP Pakistan With ARMA Models

This study focuses on forecasting monthly mean rainfall in District Dir (Lower) using various ARMA models to identify the most suitable approach for accurate rainfall prediction. Rainfall forecasting is a critical and intriguing area of study, and the Box-Jenkins methodology, which employs ARMA models, is highly effective for analyzing and forecasting time series with diverse patterns of variation. The ARMA modeling process involves defining, estimating, diagnosing, and forecasting stages, making it well-suited for this application. Based on the analysis, the ARMA (4,4) model emerged as the best fit for the dataset, evaluated using SIGMASQ, AIC, and SC criteria, which yielded the smallest values and confirmed through the Ljung-Box Q-test. Using the ARMA (4,4) model, the study successfully forecasted monthly mean rainfall in District Dir (lower) from June 2022 to May 2028.

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Publication Details

Journal
Journal of Asian Development Studies
Published
2025-03-01
DOI
https://doi.org/10.62345/jads.2025.14.1.57
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
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article

Forecasting of Monthly Rainfall in Dir (L) KP Pakistan With ARMA Models

Aimal Khan, Muhammad Ismail, Habib Ullah Khan
Journal of Asian Development Studies
Energy Load and Power Forecasting
article

Forecasting of Monthly Rainfall in Dir (L) KP Pakistan With ARMA Models

Aimal Khan, Muhammad Ismail, Habib Ullah Khan
article en

Abstract

This study focuses on forecasting monthly mean rainfall in District Dir (Lower) using various ARMA models to identify the most suitable approach for accurate rainfall prediction. Rainfall forecasting is a critical and intriguing area of study, and the Box-Jenkins methodology, which employs ARMA models, is highly effective for analyzing and forecasting time series with diverse patterns of variation. The ARMA modeling process involves defining, estimating, diagnosing, and forecasting stages, making it well-suited for this application. Based on the analysis, the ARMA (4,4) model emerged as the best fit for the dataset, evaluated using SIGMASQ, AIC, and SC criteria, which yielded the smallest values and confirmed through the Ljung-Box Q-test. Using the ARMA (4,4) model, the study successfully forecasted monthly mean rainfall in District Dir (lower) from June 2022 to May 2028.

Journal of Asian Development StudiesVol. 14(1)
Quaid-i-Azam University (PK), University of Peshawar (PK), University of Malakand (PK)
Openalex Percentile: Top 96%
Energy Load and Power Forecasting
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