With time series forecasts, models incorporate various features to improve prediction accuracy. Feature coefficients are critical to interpret forecast results.
Explainability coefficients may be generated for:
- Related data line items
- Historical data for the target value (actuals)
- Seasonal and trend components
Positive coefficients
A coefficient with a value greater than zero means there's a positive relationship between explainability data features and the historical data . As the value of the related data line item increases, the value of the historical data increases as well. Examples:
- A positive coefficient for a lagged value suggests that the time series (past values) correlate with future values.
- A positive coefficient for a seasonal component means a positive impact on the target value.
Negative coefficients
A coefficient with a value less than zero means a negative relationship between the explainability feature and the historical data. As the value of the related data increases, the value of the historical data decreases. Examples:
- A negative coefficient for a competitor's sales means that their increased sales are associated with a decrease in your sales.
- A negative coefficient for a seasonal component means that the season has a negative impact on the target value.
