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.
| Published in | Mathematical Modelling and Applications (Volume 5, Issue 1) |
| DOI | 10.11648/j.mma.20200501.14 |
| Page(s) | 39-46 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Arima, Sarima, VAR, Rainfall Data
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APA Style
Mawora Thomas Mwakudisa, Edgar Ouko Otumba, Joyce Akinyi Otieno. (2020). Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014. Mathematical Modelling and Applications, 5(1), 39-46. https://doi.org/10.11648/j.mma.20200501.14
ACS Style
Mawora Thomas Mwakudisa; Edgar Ouko Otumba; Joyce Akinyi Otieno. Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014. Math. Model. Appl. 2020, 5(1), 39-46. doi: 10.11648/j.mma.20200501.14
AMA Style
Mawora Thomas Mwakudisa, Edgar Ouko Otumba, Joyce Akinyi Otieno. Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014. Math Model Appl. 2020;5(1):39-46. doi: 10.11648/j.mma.20200501.14
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Download@article{10.11648/j.mma.20200501.14,
author = {Mawora Thomas Mwakudisa and Edgar Ouko Otumba and Joyce Akinyi Otieno},
title = {Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014},
journal = {Mathematical Modelling and Applications},
volume = {5},
number = {1},
pages = {39-46},
doi = {10.11648/j.mma.20200501.14},
url = {https://doi.org/10.11648/j.mma.20200501.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mma.20200501.14},
abstract = {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.},
year = {2020}
}
TY - JOUR T1 - Fitting Time-series Models to Kisumu Rainfall Data for the Period 1961-2014 AU - Mawora Thomas Mwakudisa AU - Edgar Ouko Otumba AU - Joyce Akinyi Otieno Y1 - 2020/02/19 PY - 2020 N1 - https://doi.org/10.11648/j.mma.20200501.14 DO - 10.11648/j.mma.20200501.14 T2 - Mathematical Modelling and Applications JF - Mathematical Modelling and Applications JO - Mathematical Modelling and Applications SP - 39 EP - 46 PB - Science Publishing Group SN - 2575-1794 UR - https://doi.org/10.11648/j.mma.20200501.14 AB - 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. VL - 5 IS - 1 ER -
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