Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014
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 therefo...
| Autores principales: | , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Science Publishing Group
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/109943 |
| _version_ | 1855538515450789888 |
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| author | Mwakudisa, Mawora Thomas Otumba, Edgar Ouko Otieno, Joyce Akinyi |
| author_browse | Mwakudisa, Mawora Thomas Otieno, Joyce Akinyi Otumba, Edgar Ouko |
| author_facet | Mwakudisa, Mawora Thomas Otumba, Edgar Ouko Otieno, Joyce Akinyi |
| author_sort | Mwakudisa, Mawora Thomas |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace109943 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Science Publishing Group |
| publisherStr | Science Publishing Group |
| record_format | dspace |
| spelling | CGSpace1099432024-01-23T12:04:57Z Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014 Mwakudisa, Mawora Thomas Otumba, Edgar Ouko Otieno, Joyce Akinyi rainfall patterns forecasting smallholders food security climate change agriculture 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. 2020 2020-10-22T17:15:48Z 2020-10-22T17:15:48Z Journal Article https://hdl.handle.net/10568/109943 en Open Access Science Publishing Group Mwakudisa MT, Otumba EO, Otieno JA. 2020. Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014. Mathematical Modelling and Applications 5(1):39-46. |
| spellingShingle | rainfall patterns forecasting smallholders food security climate change agriculture Mwakudisa, Mawora Thomas Otumba, Edgar Ouko Otieno, Joyce Akinyi Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014 |
| title | Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014 |
| title_full | Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014 |
| title_fullStr | Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014 |
| title_full_unstemmed | Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014 |
| title_short | Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014 |
| title_sort | fitting time series models to kisumu rainfall data for the period 1961 2014 |
| topic | rainfall patterns forecasting smallholders food security climate change agriculture |
| url | https://hdl.handle.net/10568/109943 |
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