Advanced Time Series Forecasting Toolkit

Build a comprehensive time series forecasting library that supports multiple advanced prediction techniques, including ARIMA, Prophet, LSTM neural networks, and ensemble methods. Create a modular system that can automatically select the most appropriate forecasting model based on dataset characteristics, with built-in model performance evaluation and confidence interval generation. Implement automatic feature engineering, seasonality detection, and support for handling missing data and external regressors.