Deep Learning Based Dynamic Pricing Model for Hotel Revenue Management Dynamic pricing, which determines optimal prices of products or services, is a research topic that has received a considerable amount of attention from different scientific communities: operations research and management science, marketing, economics, econometrics, and computer science. In this paper, we describe a dynamic pricing system for hotel revenue management. The proposed dynamic pricing system consists of three parts. The first one analyzes a hotel and its competitors so that we can set a reasonable base price. The second part forecasts future occupancy of a hotel, in this part we propose a novel sequence learning model for occupancy prediction, which integrates Deep FactorizationMachine (DeepFM) and the seq2seq model. Compared with several classical methods for time series prediction, our proposed model can significantly improve the performance of hotel occupancy forecasting without very complex feature engineering. The third part of the dynamic pricing system adopts a Deep Neural Network (DNN) to model human expertise and make adjustment for the hotel room price according to the base price, the predicted occupancy and other relating factors. We compared our dynamic pricing system with a rule based pricing strategy made by revenue management experts for evaluation, the results show that our model can suggest more rational price adjustment and make hotel revenue management more efficient.

Enhancing Profit by Predicting Stock Prices using Deep Neural Networks Financial time series forecasting is a challenging task, which has attracted the interest of several researchers and is immensely important for investors. In this paper, we present a deep learning system, which uses a variety of data for a subset of the stocks on the NASDAQ exchange to forecast the stock price. The prediction model is trained on the minutely data for a specific stock ticker and predicts the closing price of that stock ticker for multistepahead. Our deep learning framework consists of a Variational Autoencoder for removing noise and uses timeseries data engineering to combine the higherlevel features with the original features. This new set of features is fed to a Stacked LSTM Autoencoder for multistepahead prediction of the stock closing price. Besides, this prediction is used by a profitmaximization strategy to provide advice on the appropriate time for buying and selling a specific stock. Results show that the proposed framework outperforms the stateoftheart time series forecasting approaches with respect to predictive accuracy and profitability.