How do you approach time series forecasting problems?

Time series forecasting requires understanding temporal patterns and selecting appropriate models based on data characteristics.\n\n• Components: Trend, seasonality, cyclical patterns, irregular fluctuations\n• Traditional methods: ARIMA, exponential smoothing, seasonal decomposition\n• Modern approaches: LSTM, Prophet, ensemble methods\n• Validation: Time-based splits, walk-forward validation, forecast accuracy metrics\n\nExample: Sales forecasting analyzes historical trends and seasonality, applies seasonal decomposition, builds ARIMA model for baseline, implements LSTM for complex patterns, and validates using rolling window approach with MAPE and MAE metrics.