Combining group method of data handling models using artificial bee colony algorithm for time series forecasting

Time series forecasting which uses models to predict future values based on some historical data is an important area of forecasting, and has gained the attention of researchers from various related fields of study. In line with its popularity, various models have been introduced for producing accurate time series forecasts. However, to produce an accurate forecast is not an easy feat especially when dealing with nonlinear data due to the abstract nature of the data. In this study, a model for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and Group Method of Data Handling (GMDH) models with variant transfer functions, namely polynomial, sigmoid, radial basis function and tangent was developed. Initially, in this research, the GMDH models were used to forecast the time series data followed by each forecast that was combined using ABC. Then, the ABC produced the weight for each forecast before aggregating the forecasts. To evaluate the performance of the developed GMDH-ABC model, input data on tourism arrivals (Singapore and Indonesia) and airline passengers’ data were processed using the model to produce reliable forecast on the time series data. To validate the evaluation, the performance of the model was compared against benchmark models such as the individual GMDH models, Artificial Neural Network (ANN) model and combined GMDH using simple averaging (GMDH-SA) model. Experimental results showed that the GMDH-ABC model had the highest accuracy compared to the other models, where it managed to reduce the Root Mean Square Error (RMSE) of the conventional GMDH model by 15.78% for Singapore data, 28.2% for Indonesia data and 30.89% for airline data. As a conclusion, these results demonstrated the reliability of the GMDH-ABC model in time series forecasting, and its superiority when compared to the other existing models

Tourism forecasting using hybrid modified empirical mode decomposition and neural network

Due to the dynamically increasing importance of the tourism industry worldwide, new approaches for tourism demand forecasting are constantly being explored especially in this Big Data era. Hence, the challenge lies in predicting accurate and timely forecast using tourism arrival data to assist governments and policy makers to cater for upcoming tourists. In this study, a modified Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN) model is proposed. This new approach utilized intrinsic mode functions (IMF) produced via EMD by reconstructing some IMFs through trial and error method, which is referred to in this research as decomposition. The decomposition and the remaining IMF components are then predicted respectively using ANN model. Lastly, the forecasted results of each component are aggregated to create an ensemble forecast for the tourism time series. The data applied in this experiment are monthly tourist arrivals from Singapore and Indonesia from the year 2000 to 2013 whereby the evaluations of the model’s performance are done using two wellknown measures; RMSE and MAPE. Based on the empirical results, the proposed model outperformed both the individual ANN and EMD-ANN models

Group Method of Data Handling with Artificial Bee Colony in Combining Forecasts

In this study, the use of Artificial Intelligence (AI) algorithm to combine several time series forecasts is presented. This study is done by combining individual forecasts of Group Method of Data Handling models using the weighted-based combine approach. The weights for each individual model are calculated using Artificial Bee Colony algorithm. In order to evaluate the proposed model, this study tested the proposed model on the International Airline Passengers data, and the performances are calculated using mean square error (MSE), mean average error (MAE) and mean average percentage error (MAPE). The accuracy of the proposed model is compared to the individual models and the models implemented in previous literatures. The results revealed that the proposed model is able to produce significantly accurate forecast