The precipitation estimates are considered to be very important in economic planning. Major economic sectors highly depend on the precipitation levels. These sectors include agriculture, tourism, mining and transport. In Kenya, rainfall amount fluctuates with time hence depending on empirical observations while predicting is a hard task. Various statistical techniques have been used in forecasting precipitation. Among these techniques is Holt Winters procedures and SARIMA due to the seasonality effect. SARIMA model has been found to be effective in forecasting precipitation. The model has therefore been the most commonly used while precipitation forecasts are required. However, there is no any statistical research that has been carried out to test the effectiveness of neural networks in forecasting precipitation. This research hence considered forecasting precipitation using SARIMA and TLFN models. Box-Jenkins procedures of forecasting were used. Comparison of forecasts from the two techniques was done through the use of Mean Absolute Deviation (MAD), Mean Squared Deviation (MSD) and Mean Absolute Percentage Error (MAPE) in order to conclude which technique gives the better forecasts. Time Lagged Feed forward Neural Network model performed better than Seasonal Autoregressive Integrated Moving Average.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 5, Issue 6) |
| DOI | 10.11648/j.ajtas.20160506.15 |
| Page(s) | 359-364 |
| 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), 2024. Published by Science Publishing Group |
Precipitation, Seasonal Autoregressive Integrated Moving Average (SARIMA), Time Lagged Feed forward Neural Network (TLFN), Forecasting
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APA Style
Elias Kimani Karuiru, George Otieno Orwa, John Mwaniki Kihoro. (2016). Sarima Versus Time Lagged Feedforward Neural Networks in Forecasting Precipitation. American Journal of Theoretical and Applied Statistics, 5(6), 359-364. https://doi.org/10.11648/j.ajtas.20160506.15
ACS Style
Elias Kimani Karuiru; George Otieno Orwa; John Mwaniki Kihoro. Sarima Versus Time Lagged Feedforward Neural Networks in Forecasting Precipitation. Am. J. Theor. Appl. Stat. 2016, 5(6), 359-364. doi: 10.11648/j.ajtas.20160506.15
AMA Style
Elias Kimani Karuiru, George Otieno Orwa, John Mwaniki Kihoro. Sarima Versus Time Lagged Feedforward Neural Networks in Forecasting Precipitation. Am J Theor Appl Stat. 2016;5(6):359-364. doi: 10.11648/j.ajtas.20160506.15
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Download@article{10.11648/j.ajtas.20160506.15,
author = {Elias Kimani Karuiru and George Otieno Orwa and John Mwaniki Kihoro},
title = {Sarima Versus Time Lagged Feedforward Neural Networks in Forecasting Precipitation},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {5},
number = {6},
pages = {359-364},
doi = {10.11648/j.ajtas.20160506.15},
url = {https://doi.org/10.11648/j.ajtas.20160506.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160506.15},
abstract = {The precipitation estimates are considered to be very important in economic planning. Major economic sectors highly depend on the precipitation levels. These sectors include agriculture, tourism, mining and transport. In Kenya, rainfall amount fluctuates with time hence depending on empirical observations while predicting is a hard task. Various statistical techniques have been used in forecasting precipitation. Among these techniques is Holt Winters procedures and SARIMA due to the seasonality effect. SARIMA model has been found to be effective in forecasting precipitation. The model has therefore been the most commonly used while precipitation forecasts are required. However, there is no any statistical research that has been carried out to test the effectiveness of neural networks in forecasting precipitation. This research hence considered forecasting precipitation using SARIMA and TLFN models. Box-Jenkins procedures of forecasting were used. Comparison of forecasts from the two techniques was done through the use of Mean Absolute Deviation (MAD), Mean Squared Deviation (MSD) and Mean Absolute Percentage Error (MAPE) in order to conclude which technique gives the better forecasts. Time Lagged Feed forward Neural Network model performed better than Seasonal Autoregressive Integrated Moving Average.},
year = {2016}
}
TY - JOUR T1 - Sarima Versus Time Lagged Feedforward Neural Networks in Forecasting Precipitation AU - Elias Kimani Karuiru AU - George Otieno Orwa AU - John Mwaniki Kihoro Y1 - 2016/11/07 PY - 2016 N1 - https://doi.org/10.11648/j.ajtas.20160506.15 DO - 10.11648/j.ajtas.20160506.15 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 359 EP - 364 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20160506.15 AB - The precipitation estimates are considered to be very important in economic planning. Major economic sectors highly depend on the precipitation levels. These sectors include agriculture, tourism, mining and transport. In Kenya, rainfall amount fluctuates with time hence depending on empirical observations while predicting is a hard task. Various statistical techniques have been used in forecasting precipitation. Among these techniques is Holt Winters procedures and SARIMA due to the seasonality effect. SARIMA model has been found to be effective in forecasting precipitation. The model has therefore been the most commonly used while precipitation forecasts are required. However, there is no any statistical research that has been carried out to test the effectiveness of neural networks in forecasting precipitation. This research hence considered forecasting precipitation using SARIMA and TLFN models. Box-Jenkins procedures of forecasting were used. Comparison of forecasts from the two techniques was done through the use of Mean Absolute Deviation (MAD), Mean Squared Deviation (MSD) and Mean Absolute Percentage Error (MAPE) in order to conclude which technique gives the better forecasts. Time Lagged Feed forward Neural Network model performed better than Seasonal Autoregressive Integrated Moving Average. VL - 5 IS - 6 ER -
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