Application of Seasonal Autoregressive Moving Average Models to Analysis and Forecasting of Time Series Monthly Rainfall Patterns in Embu County, Kenya
Tartisio Njoki Filder, Moses Mahugu Muraya, Robert Mathenge Mutwiri
Asian Journal of Probability and Statistics · pp. 1–15 · Published 19 Aug 2019
10.9734/ajpas/2019/v4i430123Abstract
Rainfall is of critical importance for many people, particularly those whose livelihoods depend on rain-fed agriculture. Predicting the trend of rainfall is a difficult task, and statistical approaches such as time series analysis provide a means for predicting the patterns of rainfall. The models also offer the potential to improve areas such as increased food production, profitability, and improved food security policing. However, these forecasts and information systems may, in some instances, not be suitable for direct use by stakeholders in their decision-making. The objective of this study was to investigate rainfall variability and develop a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model for fitting the monthly rainfall using time series data. Secondary monthly data from 1998 to 2017 for Embu County was collected from the Kenya Meteorological Department, Embu and recorded into an excel sheet. R-software was utilized to analyse data for descriptive statistics, rainfall variability, and model fitting. The coefficient of variation for annual and seasonal rainfall was calculated. The Box Jenkin's ARIMA modelling procedure (model identification, model estimation, model validation) was used to determine the best models for the data. The main study findings indicated the existence of annual variability of 34%, March-April-May rainfall variability of 44%, and October-November-December variability of 44%. A first-order differenced SARIMA (1, 1, 1) (0, 1, 2)12 model with an AIC score of 9.99356 was found suitable for predicting rainfall pattern in Embu, County. The study outcome revealed that Embu County experiences high seasonal and rainfall variation of rainfall, thus requires a reliable model for better prediction.
Cited by 10
Linnet Chege, P. Gachoki, Joseph Esekon · American Journal of Theoretical and Applied Statistics · 2026
Mustafa Özdemir, Onur Şahin, M. U. Çınar · Buffalo Bulletin · 2024
Bontogho Tog-Noma Patricia Emma, Maré Boussa Tockville, Yangouliba Gnibga Issoufou · World Journal of Advanced Research and Reviews · 2023
T. M. Wanjuki, Adolphus Wagala, Dennis K. Muriithi · European Journal of Mathematics and Statistics · 2021
Fadli Irsyad, H. Oue · Paddy and Water Environment · 2021
Shakir Khan, Hela Alghulaiakh · 2020
H. Salman, Farhan
Hajar Errahmani, Mohamed Dakkon
M. A. Jayaram · Communications in Computer and Information Science · 2024
Nilesh B. Korade, Mohd. Zuber · Lecture Notes in Electrical Engineering · 2024
Related research
- Time-Series Modeling and Short Term Prediction of Annual Temperature Trend on Coast Libya Using the Box-Jenkins ARIMA Model — shares topic coverage
- Threat to the Sustainable Production of Natural Resins: A Case of Rangeeni Strain of Kerria lacca (Kerr) in Eastern Plateau & Hills Region of South East Asia — shares topic coverage
- Forecasting Student Enrollment Trends in Hotel Catering and Institutional Management at Kumasi Technical University — shares topic coverage
- Spatio-temporal Analysis of Land Cover Changes of Izmir Province of Turkey Using Landsat TM and OLI Imagery — shares topic coverage
- Automating Behavior? An Experimental Living Lab Study on the Effect of Smart Home Systems and Traffic Light Feedback on Heating Energy Consumption — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
10
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.