Hotel Occupancy Forecasting System
Design a high-scale machine learning system to predict the daily occupancy rate for a global hotel chain (100k+ properties) over a 30-day horizon. The system must integrate diverse data sources including real-time 'On-the-Books' reservation data, historical occupancy trends, and external market signals like local events and competitor pricing. Your design should address time-series specific challenges such as temporal leakage, lead-time dynamics, and cold-start problems for new hotels. Focus on the end-to-end lifecycle including data ingestion, feature engineering for time-series, batch inference scalability, and strategies for handling uncertainty in forecasts.
LightGBMXGBoostSparkKafkaDelta LakeFeastDynamoDBQuantile RegressionConformal PredictionFourier Transforms
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