1. Time Series Analysis 1.1 Classical Methods ARIMA (AutoRegressive Integrated Moving Average) A statistical model that combines autoregression, differencing, and moving average components for time series forecasting. Use Cases: Financial forecasting Sales prediction Weather forecasting Demand planning Traffic prediction Strengths: Handles trends and seasonality Well-understood statistical properties Good for linear relationships Interpretable components Works with stationary data Limitations: Assumes linear relationships Requires stationarity Limited with complex patterns Sensitive to outliers Not suitable for long-term forecasting…