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Abstract            Volume:8  Issue-11  Year-2020         Original Research Articles


Online ISSN : 2347 - 3215
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcret@gmail.com

Time series Analysis of Monthly Average Temperature and Rainfall Using Seasonal ARIMA Model (In Case of Ambo Area, Ethiopia)
Teshome Hailemeskel Abebe*
Department of Economics, College of Business and Economics, Ambo University, Ambo, Ethiopia
*Corresponding author
Abstract:

Forecasting mean temperature and rainfall is an important for planning and formulating agricultural strategies. Thus, this paper, try to analyze and forecast monthly mean temperature and rainfall in Ambo area on the data from January 2012 to March 2019.From graphical analysis on time plot and ACF, the series seems to have a seasonal component. For that purpose, a Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to estimate and forecast the average monthly temperature and rainfall in the Ambo area, Ethiopia. Among the competitive tentative model, SARIMA (2, 0, 1) (2, 0, 1)12 and SARIMA (1, 0, 1) (1, 0, 1)12 model are the best time series model for fitting and forecasting mean temperature and rainfall, respectively. Moreover, the model diagnostic test on the residuals of SARIMA (2, 0, 1) (2, 0, 1)12 and SARIMA (1, 0, 1) (1, 0, 1)12) on mean temperature and rainfall satisfies the randomness, independency, normality and constant variance (homoscedasticity) assumptions. Finally, SARIMA (2, 0, 1) (2, 0, 1)12 and SARIMA (1, 0, 1) (1, 0, 1)12 were used to forecast mean of monthly temperature and rainfall from the period April 2019 to March 2023.

Keywords: Temperature, Rainfall, SARIMA, Modeling, Forecasting
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How to cite this article:

Teshome Hailemeskel Abebe. 2020. Time series Analysis of Monthly Average Temperature and Rainfall Using Seasonal ARIMA Model (In Case of Ambo Area, Ethiopia).Int.J.Curr.Res.Aca.Rev. 8(11): 31-47
doi: https://doi.org/10.20546/ijcrar.2020.811.005
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.