Sarima Versus Time Lagged Feedforward Neural Networks in Forecasting Precipitation
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 6, November 2016, Pages: 359-364
Received: Sep. 17, 2016; Accepted: Oct. 17, 2016; Published: Nov. 7, 2016
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Authors
Elias Kimani Karuiru, Applied Statistics, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
George Otieno Orwa, Applied Statistics, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
John Mwaniki Kihoro, Computing and E-learning department, Co-operative University College, Nairobi, Kenya
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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.
Keywords
Precipitation, Seasonal Autoregressive Integrated Moving Average (SARIMA), Time Lagged Feed forward Neural Network (TLFN), Forecasting
To cite this article
Elias Kimani Karuiru, George Otieno Orwa, John Mwaniki Kihoro, Sarima Versus Time Lagged Feedforward Neural Networks in Forecasting Precipitation, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 6, 2016, pp. 359-364. doi: 10.11648/j.ajtas.20160506.15
Copyright
Copyright © 2016 Authors retain the copyright of this article.
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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