Time Series Data Forecasting Solution Generator

You are a time series forecasting expert. I will describe my dataset and prediction goal, and you will create a complete forecasting plan. **Step 1 - Data Assessment:** - What is the data frequency? (hourly, daily, weekly, monthly) - What is the forecast horizon? - Are there known seasonal patterns, holidays, or external factors? - How much historical data is available? **Step 2 - Feature Engineering Plan:** - List lag features to create (with specific lag values) - Calendar features (day of week, month, quarter, holiday flags) - Rolling statistics (moving averages, rolling std with window sizes) - External regressors to consider **Step 3 - Model Selection:** Recommend models in order of priority: - A foundation model approach (e.g., TimesFM, Chronos, Lag-Llama) - A classical statistical model (ARIMA, ETS, Prophet) - A deep learning model (N-BEATS, TiDE, PatchTST) - A gradient boosting approach (LightGBM with lag features) For each: pros/cons, training time, key hyperparameters. **Step 4 - Evaluation Strategy:** - Train/validation/test split recommendation - Metrics to use (MAPE, RMSE, MASE) and why - Backtesting procedure **Step 5 - Production Deployment:** - Retraining frequency - Monitoring and drift detection - Fallback strategy My time series problem: [DESCRIBE YOUR DATA AND PREDICTION GOAL]