Volume 5, Issue 1, March 2020, Page: 39-46
Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014
Mawora Thomas Mwakudisa, Department of Statistics and Actuarial Science, School of Mathematics, Statistics and Actuarial Science, Maseno University, Maseno, Kenya
Edgar Ouko Otumba, Department of Statistics and Actuarial Science, School of Mathematics, Statistics and Actuarial Science, Maseno University, Maseno, Kenya
Joyce Akinyi Otieno, Department of Statistics and Actuarial Science, School of Mathematics, Statistics and Actuarial Science, Maseno University, Maseno, Kenya
Received: Jan. 16, 2020;       Accepted: Feb. 6, 2020;       Published: Feb. 19, 2020
DOI: 10.11648/j.mma.20200501.14      View  278      Downloads  104
Many small-scale farmers require adequate forecasts to help them plan for the rainfall. The National Meteorological Service provides forecasts seasonally, monthly and weekly. The forecasts are qualitative in nature hence inform, but cannot be directly used with decision support models. It is therefore important to consider forecast methods that researchers can use to generate quantitative data that can be applied in the models. In particular, an increasing need for forecasting daily rainfall data. In this study, the ARIMA and VAR models have been used to forecast five time period data for daily, monthly and seasonal rainfall data. The objective was to find the model parameters that best fit the three time periods. Fifty-year data from Kenya Meteorological Station, Kisumu, was used for the analysis. For each time period, five events were used as the test dataset. The ARIMA model was found to be best for forecasting daily rainfall in comparison to the VAR model, while SARIMA was best for monthly and seasonal data. One difference was done for the seasonal rainfall total, but not for monthly and monthly rainfall data. The VAR models included the available daily minimum and maximum temperatures. However, forecasted daily rainfall deviated from the test data, while monthly and seasonal data deviated even more.
Arima, Sarima, VAR, Rainfall Data
To cite this article
Mawora Thomas Mwakudisa, Edgar Ouko Otumba, Joyce Akinyi Otieno, Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014, Mathematical Modelling and Applications. Vol. 5, No. 1, 2020, pp. 39-46. doi: 10.11648/j.mma.20200501.14
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This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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