Hotel yield prediction method, system and device and storage medium

Abstract

The invention provides a hotel yield prediction method, system and device and a storage medium, and the method comprises the steps: obtaining the yield data of a plurality of hotels, wherein the yielddata comprises historical yield data, hotel attribute feature data and current reservation progress data; constructing a quantile loss function based on the quantile regression model; constructing aninitial deep learning network model; based on the quantile loss function and the yield data of the plurality of hotels, training the initial deep learning network model, and respectively obtaining atarget network model corresponding to each hotel; predicting the yields of the plurality of hotels in a future preset time period based on the target network model, and outputting the yield value of each hotel in the preset time period; according to the invention, the yield of multiple hotels can be predicted at the same time, and the hotel yield prediction accuracy is improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method, system, equipment and storage medium for predicting hotel output. Background technique [0002] In the prior art, the traditional time series model or the neural network model LSTM (Long Short-Term Memory, artificial neural network of long short-term memory) is generally used to predict the output of the hotel (that is, the sales of resources such as hotel rooms). However, the traditional time series model is difficult to comprehensively consider the impact of multiple exogenous variables on the final yield, and its expressive ability is limited. In the process of hotel output forecasting, in addition to time series features, it also involves exogenous variables such as scheduled schedule features, hotel competition circle features, hotel attribute features, date features, and other exogenous variables. Therefore, at this time, the prediction ac...

AI Technical Summary

Problems solved by technology

Therefore, the prediction accuracy of the traditional time series model will be significantly worse at this time.

However, the neural network model LSTM has limited expression ability in the scenario of predicting multiple hotels, and cannot be used in the scenario of predicting multiple hotels at the same time.

Images