Time Series Forecasting Guide
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Submitted Jul 18AI evaluated Jul 18
Prompt
Build time series forecasting model.
<time_series_data>
- Frequency: {hourly/daily/monthly}
- Length: {number of periods}
- Seasonality: {patterns observed}
- Trend: {increasing/decreasing/stable}
</time_series_data>
<external_factors>
{holidays, events, weather, etc}
</external_factors>
<forecast_requirements>
- Horizon: {how far ahead}
- Accuracy needs: {error tolerance}
- Update frequency: {how often retrained}
</forecast_requirements>
Build forecast:
1. Data preparation
- Missing value handling
- Outlier treatment
- Stationarity testing
- Decomposition
2. Model selection
- ARIMA variations
- Exponential smoothing
- Prophet/Neural Prophet
- LSTM if appropriate
3. Feature engineering
- Lag features
- Rolling statistics
- External regressors
- Calendar features
4. Validation strategy
- Time series split
- Walk-forward analysis
- Prediction intervals
Include code implementation.
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