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- W3033991223 abstract "In recent years, with the rapid development of the Internet in China, online transactions have grown greatly. For example, OTAs with a large number of hotels have accumulated a large amount of hotel data and user consumption data. And the online sales of hotels is the basis and core of revenue management. Time series prediction has always been one of the main application fields of machine learning algorithm. From the classical traditional time series prediction methods to long-term and short-term memory networks and closed-loop neural networks, the prediction ability is constantly improving. With the development of deep neural networks, convolution neural networks show superior performance in the prediction of time series. This paper proposes a new prediction model based on the improved WaveNet using not only the parameters of historical sales and hotel property, but also the parameters of holiday time and time position in the prediction range, which are processed by serialization. Simulation results are presented in details in this paper, where these results indicate the effectiveness of the proposed forecasting tool as an accurate technique." @default.
- W3033991223 created "2020-06-12" @default.
- W3033991223 creator A5060232754 @default.
- W3033991223 date "2020-05-01" @default.
- W3033991223 modified "2023-09-24" @default.
- W3033991223 title "Research on hotel online sales forecast model based on improved WaveNet" @default.
- W3033991223 cites W2893159103 @default.
- W3033991223 doi "https://doi.org/10.1088/1742-6596/1544/1/012067" @default.
- W3033991223 hasPublicationYear "2020" @default.
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