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 pap...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.展开更多
In China, the proven reserves of natural gas is 3.86 trillion cubic meters (tcm). This figure will quite possibly increase at a high speed in the future. At present, the annual production of natural gas has exceeded 3...In China, the proven reserves of natural gas is 3.86 trillion cubic meters (tcm). This figure will quite possibly increase at a high speed in the future. At present, the annual production of natural gas has exceeded 34 billion cubic meters (bcm), which, likewise, will rise sharply. The pipeline construction has basically taken shape, which will be gradually perfected. And the gas consumption market is in the startup stage, which will be rapidly expanded in the quite near future.展开更多
文摘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.
文摘In China, the proven reserves of natural gas is 3.86 trillion cubic meters (tcm). This figure will quite possibly increase at a high speed in the future. At present, the annual production of natural gas has exceeded 34 billion cubic meters (bcm), which, likewise, will rise sharply. The pipeline construction has basically taken shape, which will be gradually perfected. And the gas consumption market is in the startup stage, which will be rapidly expanded in the quite near future.
