Forecast Horizon

Understanding forecast horizons

The forecast horizon is a fundamental concept in time-series analysis that defines how far into the future we attempt to predict. Shorter horizons (minutes to hours) typically yield more accurate predictions than longer horizons (months to years) due to increasing uncertainty over time.

Impact on model selection

Different forecast horizons require different modeling approaches:

1. Short-term horizons (seconds to hours):

• Ideal for real-time analytics
• Often use streaming feature extraction
• Focus on immediate market movements or sensor readings

2. Medium-term horizons (days to weeks):

• Balance between reactivity and trend analysis
• Incorporate both recent and historical patterns
• Suitable for inventory planning and risk assessment

3. Long-term horizons (months to years):

• Emphasize fundamental factors and cycles
• Require robust handling of uncertainty
• Often use ensemble methods and scenario analysis

Practical applications

Financial markets

In trading systems, forecast horizons directly influence strategy selection:

Industrial systems

Manufacturing and process control applications use varying horizons:

SELECT timestamp, tempF,
    LEAD(tempF, 12) OVER (ORDER BY timestamp) as temp_forecast_1h
FROM weather
WHERE timestamp > '2022-01-01'
LIMIT 5;

This example shows temperature forecasting with a 1-hour horizon using a simple lead calculation.

Challenges and considerations

1. Uncertainty propagation

• Longer horizons accumulate more uncertainty
• Require robust error estimation
• Need for confidence intervals widens with time

2. Data requirements

• Longer horizons typically need more historical data
• Must account for seasonality modeling
• Need to handle missing or late arriving data

3. Computational complexity

• Longer horizons often require more computational resources
• May need to balance accuracy with performance
• Consider using downsampling strategies for efficiency

Best practices

1. Match horizon to business needs

• Consider the decision-making timeframe
• Balance accuracy requirements with resource constraints
• Account for data availability and quality

2. Monitor and validate

• Track forecast accuracy across different horizons
• Implement anomaly detection for validation
• Regularly recalibrate models based on performance

3. Handle uncertainty

• Use probabilistic forecasts for longer horizons
• Implement scenario analysis for critical applications
• Consider ensemble methods for improved robustness