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Feature Engineering for Time-Series Data

Introduction

Feature engineering is crucial for extracting meaningful insights from time-series data. Here are key techniques that can enhance your time-series analysis.

1. Time-Based Features

• Timestamp Components: Extract year, month, day, hour
• Cyclical Features: Transform cyclic time components
• Holiday Indicators: Flag special dates

2. Lag Features

Create features from past values:

• Previous day's value (lag-1)
• Moving averages (7-day, 30-day)
• Rolling statistics (min, max, std)

3. Window Features

# Python example
def create_window_features(df):
    df['rolling_mean_7d'] = df['value'].rolling(7).mean()
    df['rolling_std_7d'] = df['value'].rolling(7).std()
    return df
                    

4. Domain-Specific Features

• Technical indicators for financial data
• Seasonal decomposition
• Fourier transforms for periodic patterns

Conclusion

Effective feature engineering can significantly improve your time-series models' performance. Start with basic time-based features and gradually incorporate more complex transformations based on your specific use case.

This work is licensed under a Creative Commons Attribution 4.0 International License.