Combined forecast model involving wavelet-group methods of data handling for drought forecasting

Vigorous efforts to improve the effectiveness of drought forecasting models has yet to yield accurate result. The situation gives room on the use of robust forecasting methods that could effectively improve existing methods. The complex nature of time series data does not enable one single method that is suitable in all situations. Thus, a combined model that will provide a better result is then proposed. This study introduces a wavelet and group methods of data handling (GMDH) by integrating discrete wavelet transform (DWT) and GMDH with transfer functions such as sigmoid and radial basis function (RBF) to form three wavelet-GMDH models known as modified W-GMDH (MW-GMDH), sigmoid W-GMDH (SW-GMDH) and RBF W-GMDH. To assess the effectiveness of this approach, these models were applied to rainfall data at four study stations namely Arau and Kuala Krai in Malaysia as well as Badeggi and Duku-Lade in Nigeria. These data were transformed into four Standardized Precipitation Index (SPI) known as SPI3, SPI6, SPI9 and SPI12. The result shows that the integration of DWT improved the performance of the conventional GMDH model. The combination of these models further improved the performance of each model. The proposed model provides efficient, simple, and reliable accuracy when compared with other models. The incorporation of wavelet to the study results in improving performance for all four stations with the Combined W-GMDH (CW-GMDH) and Combined Regression W-GMDH (CRW-GMDH) models. The results show that Duku-Lade station produced the lowest value of 0.0239 and 0.0211 for RMSE and MAE and highest value of 0.9858 for R respectively. In addition, CRW-GMDH model produce the lowest value of 0.0168 and 0.0117, and the highest value of 0.9870 for RMSE MAE, and R respectively. On the percentage improvement, Duku-Lade station shows improvement over other models with the reductions in RMSE and MAE by 42.3% and 80.3% respectively. This indicates that the model is most suitable for the drought forecasting in this station. The results of the comparison among the four stations indicate that the CW-GMDH and CRW-GMDH models are more accurate and perform better than MW-GMDH, SW-GMDH and RBFW-GMDH models. However, the overall performance of the CRW-GMDH model outweigh that of the CW-GMDH model. In conclusion, CRW-GMDH model performs better than other models for drought forecasting and capable of providing a promising alternative to drought forecasting technique

Combine holts winter and support vector machines in forecasting time serie

This study proposes on a combine methodology that exploits the Holts- Winter (HW) model and the Support Vector Machines (SVM) model in forecasting time series. Problems of forecasting using time series data have been and still being addressed at every sphere of research using different approaches. The performance of the forecast was compared among the three models, the HW model, the SVM model and the combine model (HW and SVM). Four different data sets namely, airline passengers’ data, machinery industry production data, clothing industry data and sugar production data were considered in the study. The statistical measures such as mean squared error (MSE), mean average error (MAE) and correlation coefficient, R, were used to evaluate the performance of the propose model. The result of this study indicated that the combine model shows an improvement of 149.3% over HW model and 35.9% improvement over the SVM model for the airline passengers’ data. The result of the machinery industry presented that the combine model shows an improvement of 93.3% over HW model and 42.8% improvement over the SVM model. In the case of the clothing industry the result shows the combine model gives an improvement of 61.6% over HW model and 12.0% improvement over SVM model. Lastly, with respect to the sugar production, the result shows that the combine model indicated an improvement of 34.4% over HW model and 25.1% improvement over SVM model. Therefore the results of the experiments suggest that the proposed combine model is more reliable in time series when compared with the individual model

Combine holts winter and support vector machines in forecasting time series

This study proposes on a combine methodology that exploits the Holts- Winter (HW) model and the Support Vector Machines (SVM) model in forecasting time series. Problems of forecasting using time series data have been and still being addressed at every sphere of research using different approaches. The performance of the forecast was compared among the three models, the HW model, the SVM model and the combine model (HW and SVM). Four different data sets namely, airline passengers’ data, machinery industry production data, clothing industry data and sugar production data were considered in the study. The statistical measures such as mean squared error (MSE), mean average error (MAE) and correlation coefficient, R, were used to evaluate the performance of the propose model. The result of this study indicated that the combine model shows an improvement of 149.3% over HW model and 35.9% improvement over the SVM model for the airline passengers’ data. The result of the machinery industry presented that the combine model shows an improvement of 93.3% over HW model and 42.8% improvement over the SVM model. In the case of the clothing industry the result shows the combine model gives an improvement of 61.6% over HW model and 12.0% improvement over SVM model. Lastly, with respect to the sugar production, the result shows that the combine model indicated an improvement of 34.4% over HW model and 25.1% improvement over SVM model. Therefore the results of the experiments suggest that the proposed combine model is more reliable in time series when compared with the individual models

A Hybrid Wavelet-Arima model for standardized precipitation index drought forecasting

This paper proposes A Hybrid Wavelet-Auto-Regressive Integrated Moving Average (W-ARIMA) model to explore the ability of the hybrid model over an ARIMA model. It combines two methods, a Discrete Wavelet Transform (DWT) and ARIMA model using the Standardized Precipitation Index (SPI) drought data for forecasting drought modeling development. SPI data from January 1954 to December 2008 used was divided into two - (80%/20% for training/testing respectively). The results were compared with the conventional ARIMA model with Mean Square Error (MSE) and Mean Average Error (MAE) as an error measure. The results of the proposed method achieved the best forecasting performance