Neural networks in applied statistics
Technometrics
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Back-Propagation: Theory, Architecture, and Applications
Back-Propagation: Theory, Architecture, and Applications
Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
Analysis and modeling of multivariate chaotic time series based on neural network
Expert Systems with Applications: An International Journal
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
A rule-based intelligent method for fault diagnosis of rotating machinery
Knowledge-Based Systems
Rainfall forecasting based on ensemble empirical mode decomposition and neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Due to the fluctuation and complexity of the tourism industry, it is difficult to capture its non-stationary property and accurately describe its moving tendency. In this study, a novel forecasting model based on empirical mode decomposition (EMD) and neural network is proposed to predict tourism demand (i.e. the number of arrivals). The proposed approach first uses EMD, which can adaptively decompose the complicated raw data into a finite set of intrinsic mode functions (IMFs) and a residue, which have simpler frequency components and higher correlations. The IMF components and residue are than modeled and forecasted using back-propagation neural network (BPN) and the final forecasting value can be obtained by the sum of these prediction results. In order to evaluate the performance of the proposed approach, the majority of international visitors to Taiwan are used as illustrative examples. Experimental results show that the proposed model outperforms the single BPN model without EMD preprocessing and the traditional autoregressive integrated moving average (ARIMA) models.