Open AccessResearch on hotel online sales forecast model based on improved WaveNet
Author(s) -
Dongmei Duan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012067
Subject(s) - computer science , serialization , artificial neural network , time series , position (finance) , artificial intelligence , the internet , revenue , series (stratigraphy) , machine learning , data mining , industrial engineering , engineering , business , accounting , finance , world wide web , economics , operating system , paleontology , biology
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.