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Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. and Kingsbury, B. (2012) Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29, 82-97.
https://doi.org/10.1109/MSP.2012.2205597

has been cited by the following article:

• TITLE: Tourism Traffic Demand Prediction Using Google Trends Based on EEMD-DBN

  AUTHORS: Yi Xiao, Xueting Tian, Ming Xiao

  KEYWORDS: Tourism Traffic Demand Forecasting, Deep Learning, Google Trends, Composite Search Index, Ensemble Empirical Mode Decomposition (EEMD), Deep Belief Network (DBN)

  JOURNAL NAME: Engineering, Vol.12 No.3, March 30, 2020

  ABSTRACT: Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.