Show simple item record

dc.contributor.authorMawora Thomas Mwakudisa, Edgar Ouko Otumba, Joyce Akinyi Otieno
dc.date.accessioned2020-08-25T08:47:51Z
dc.date.available2020-08-25T08:47:51Z
dc.date.issued2020-02-20
dc.identifier.urihttps://repository.maseno.ac.ke/handle/123456789/2343
dc.description.abstractMany 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.en_US
dc.publisherScience Publishing Groupen_US
dc.subjectArima, Sarima, VAR, Rainfall Dataen_US
dc.titleFitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014en_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record