Time Series Forecasting with Deep Learning

# Time Series Forecasting with Deep Learning Time series forecasting is crucial for trading, demand planning, and resource allocation. Here's how to use deep learning effectively. ## Understanding Time Series ### Components - **Trend**: Long-term direction - **Seasonality**: Periodic patterns - **Noise**: Random variations ### Challenges - Non-stationarity - Multiple seasonalities - Missing data - Concept drift ## Traditional Methods ### ARIMA Good baseline, interpretable, but limited for complex patterns. ### Prophet Facebook's library, handles seasonality well, good for quick prototyping. ## Deep Learning Approaches ### LSTM/GRU Recurrent neural networks for sequential data: - Capture long-term dependencies - Handle variable-length sequences ### Temporal Convolutional Networks CNN-based approach: - Parallel processing - Faster training than RNNs ### Transformers Attention-based models: - Capture global dependencies - State-of-the-art results ## Feature Engineering ### Lag Features Past values as features. ### Rolling Statistics Moving averages, standard deviations. ### Time Features Day of week, month, holiday indicators. ### External Features Weather, events, economic indicators. ## Evaluation ### Metrics - MAE: Mean Absolute Error - RMSE: Root Mean Square Error - MAPE: Mean Absolute Percentage Error ### Cross-Validation Walk-forward validation for time series - no data leakage. ## Best Practices 1. **Start simple**: Beat simple baselines first 2. **Understand your data**: Visualize before modeling 3. **Feature engineering matters**: Often more than model choice 4. **Ensemble methods**: Combine multiple approaches ## Conclusion Deep learning offers powerful tools for time series, but fundamentals still matter. Understand your data and start simple.