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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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
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.
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.
Borlando, P, R Montana and Raze (1996), ‘Forecasting hourly precipitation in time of fall using arima models’, Journal of Atmospheric Research 42(1), 199–216.
Box, G E and G M Jenkins (1970), Time Series Analysis: Forecasting and Control, Holden-Day, San Franciscoi.Heinz, G, L J Peterson, R W Johnson and Kerk C J (2003), ‘Exploring relationships in bodydimensions’, Journal of Statistics Education 11, 1–15.
Brier, G .W (1950), ‘Verification of forecasts expressed in terms of probability.’, MonthlyWeather Review 78(1), 1–3.
Chatfield, C (2000), Time-Series Forecasting, Chapman and Hall CRC, New York.
Glahn, Harry and Dale A Lowry (1972), ‘The use of model output statistics mos in objective weather forecasting’, Journal of Applied Meteorology 11, 1203–1211.
Hall, Brooks and Doswel (1999), ‘Precipitation forecasting using a neural network. weather and forecasting’, Journal of Applied Meteorology 14(3), 338–345.
Jani, P (2014), Business Statistics:Theories and Applications, PHI Learning Private Limitedl, Delhi.
Jose, Neil R Euliano and W. Curt Lefeb (2000), ‘Neural and adaptive systems: Fundamentals through simulations’, Journal of Applied Time series Analysis .
Kane, Ibrahim Lawal and Fadhilah Yusof (2012), ‘Modelling monthlyrainfall time series using ets and sarima’, International Journal of Current Research 4(1), 195–200.
Kevin, Swingler (1996), ‘Applying neural networks: A practical guide’.
Kibunja, Hellen, George Orwa and John Kihoro (2014), ‘Forecasting precipitation in mt kenya’,Journal of Applied Time series Analysis 7(2), 87–105.
Klein, William H and Frank Lewis (1970), ‘Computer forecasts of maximum and minimum temperature’, Journal of Applied Meteorology 9, 350–359.
Kuligowski and Barros (1998), ‘Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks.’, Weather and Forecasting 13(4), 1194–1204.
Luk, K.C, Ball and Sharma (2000), ‘A study of optimal lag and spatial inputs for artificial neural network for rainfall forecasting.’, Journal of Hydrology 227, 56–65.
Oliver, Anderson D (1977), ‘Time series analysis and forecasting: Another look at the box- jenkins approach’, Journal of Royal Statistical Society 26(4), 285–353.
Pankratz, A (1983), Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, John Wiley and Sons, New Yorki.
Russell, Reed and Robert J Marks II (1998), ‘Neural smithing: Supervised learning in feedfor- ward artificial neural networks’, Journal of Applied Time series Analysis 7(2).
Satya, P, V Ramasubramanian and S C Menta (2007), ‘Statistical models for forecasting milk production in india’, Journal of the Indian Society of Agricultural Statistics 6, 80–83.
Schaefar, J.T (1990), ‘The critical success index as an indicator of warning skill.’, WeatherForecasting 5(4), 570–575.
Sopipan, N (2014), ‘Forecasting rainfall in thailand: A case study of nakhon ratchasima province’, International Journal of Environmental, Chemical, Ecological, Geological and Geographical Engineering 8(11).
Wilks, D.S (1995), ‘Statistical methods in the atmospheric sciences’, Journal of Applied Mete- orology p. 467.