# Hotel Price Prediction Based on Multimodal Learning: Applications of MLP, LSTM, and Fusion Models

> This project demonstrates how to use multimodal learning methods combined with MLP, LSTM, and fusion models to predict hotel prices, integrating macroeconomic indicators and historical price data to provide data-driven decision support for revenue management.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-09T22:40:17.000Z
- 最近活动: 2026-06-09T22:50:40.596Z
- 热度: 159.8
- 关键词: 多模态学习, 酒店价格预测, LSTM, MLP, 融合模型, 时间序列预测, 收益管理, 宏观经济指标
- 页面链接: https://www.zingnex.cn/en/forum/thread/mlplstm
- Canonical: https://www.zingnex.cn/forum/thread/mlplstm
- Markdown 来源: floors_fallback

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## [Introduction] Core Overview of the Hotel Price Prediction Project Based on Multimodal Learning

Original Author/Maintainer: KMS92-L, Source Platform: GitHub, Project Title: hotel-price-prediction-multimodal. The core of this project is to use multimodal learning methods combined with MLP, LSTM, and fusion models, integrating macroeconomic indicators (such as CPI, crude oil prices) and historical hotel price data to achieve more accurate hotel price prediction and provide data-driven decision support for revenue management.

## Project Background: Challenges in Hotel Price Prediction and Applications of Multimodal Learning

Hotel price prediction is a core issue in revenue management, affecting hotel profitability and consumer decisions. Traditional methods rely on single data sources or simple time series models, making it difficult to capture complex factors. Multimodal learning builds comprehensive models by integrating data from different sources. This project explores combining macroeconomic indicators with historical price data to improve prediction accuracy using deep learning.

## Data Modalities and Feature Engineering: Integration of Macroeconomic and Hotel Price Data

**Macroeconomic Indicators**: Integrate CPIAUCSL.csv (Consumer Price Index), CUSR0000SEHF01.csv (CPI sub-item for hotel accommodation), and MCOILWTICO.csv (crude oil price index) to provide macro-environment context. **Hotel Price Data**: Serves as the target variable and basic feature.

## Model Architecture: Design Ideas for MLP, LSTM, and Fusion Models

- **MLP**: Baseline model that captures non-linear relationships between features and learns the mapping from inputs to prices via a fully connected network. - **LSTM**: Handles time series characteristics; its gating mechanism captures long-term trends and seasonality, avoiding gradient vanishing. - **Fusion Model**: Core innovation, which may adopt early (input layer concatenation), mid-term (post-encoding fusion), or late (weighted after independent prediction) fusion strategies to capture both macro trends and micro fluctuations.

## Technical Implementation: Key Steps for Data Preprocessing and Model Training

**Data Preprocessing**: Time series alignment, missing value interpolation/forward filling, indicator standardization. **Feature Engineering**: Construct price lag terms, rolling statistics (moving average/standard deviation), macroeconomic derived features (inflation rate, price change rate). **Model Training**: Time series cross-validation (to prevent data leakage), early stopping mechanism (to prevent overfitting), hyperparameter tuning (network structure, learning rate, etc.).

## Application Scenarios and Business Value: Decision Support for Dynamic Pricing and More

- **Dynamic Pricing**: Predict price trends, formulate peak price increases and off-season discount strategies to maximize revenue. - **Inventory Management**: Optimize room allocation by combining demand forecasting, balancing supply between direct sales and OTA channels. - **Investment Decisions**: Assist hotel investors in evaluating potential returns of regional/type hotels. - **Consumer Assistance**: Provide booking timing recommendations to help users get discounted prices.

## Technical Insights and Expansion Directions: Versatility of Multimodal Learning and Future Directions

- **Multimodal Versatility**: Can be extended to fields such as airfare, housing price, and stock price prediction. - **Combination of Deep Learning and Traditional Methods**: Choose models based on data characteristics instead of blindly pursuing complex architectures. - **Value of External Data**: Macroeconomic data significantly improves prediction capabilities; relevant external signals should be introduced when building models.

## Project Conclusion: Potential and Reference Value of Multimodal Learning in Hotel Price Prediction

This project demonstrates the potential of multimodal learning in hotel price prediction by integrating MLP, LSTM, and fusion models. By combining macroeconomic and historical price data to capture multiple influencing factors, it provides decision support for revenue management. It offers a valuable reference implementation for developers/researchers in time series prediction, revenue management, or multimodal learning.