MODEL GOVERNANCE
Seasonal model recalibration: adapt to the seasons before accuracy drops
Seasonal model recalibration with TIMi adapts a predictive model to variations that return predictably from one season to the next, instead of letting its performance decay at each change of season, and relies on the Drift Report of Modeler to pick the right moment.
Seasonal cycles
Climate, activity cycle or commercial period.
Drift Report
Shows when performance starts to move away from the reference.
Targeted adjustment
Adjust the model instead of rebuilding it from zero.
HOW IT WORKS
How seasonal model recalibration works.
Three steps, in one engine, on a standard server with no cluster.
1. Follow performance over time
The Drift Report shows when the performance of the model in production moves away from its reference, including drops that coincide with a change of season.
2. Separate seasonal from structural
A drop that returns at the same period every year points to a predictable seasonal effect. It has to be told apart from a deeper, lasting drift of the behavior modeled.
3. Recalibrate
Instead of rebuilding the model from zero, adjust it to the new seasonal conditions, using the history of previous cycles. At SANY, engineers deployed season-specific models.
Drift ReportHOW IT DIFFERS
Seasonal drift is a special case of drift.
Because it is predictable, it can be handled before it hurts.
Predictable, so anticipate it
Drift monitoring detects any loss of performance, whatever its cause. Seasonal recalibration is a special case: a recurring, predictable drift linked to a known cycle, which calls for an anticipated adjustment rather than a reaction afterwards.
You need enough cycles
A minimum number of comparable cycles is needed to tell a recurring seasonal effect from a one-off variation. The exact number depends on the stability of the phenomenon observed.
Seasonal or structural?
Comparing the current period with the equivalent periods of previous cycles helps to tell the two apart. A full retraining remains necessary if the drift turns out to be structural rather than cyclical.
EXPLAINABILITY
A seasonal drop should be understood, not just repaired.
A model that decays with the seasons, without the cause being identified, often leads to a full retraining that costs time and resources. A white-box model lets you check that the drop matches an identifiable seasonal effect, which justifies a targeted recalibration instead of a full rebuild.
For data teams
A targeted recalibration saves the time and resources of a full rebuild.
For business owners
Performance stays steady across the year, and the reason for an adjustment can be explained.
For model owners
Modeler’s built-in Drift Report shows when a model loses accuracy, so you update it when needed instead of on a fixed calendar.
DATA
What data seasonal recalibration needs.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Performance history
Model performance over several seasonal cycles.
Current period data
To compare with the previous cycles.
Equivalent past periods
Same season, previous years.
Known seasonal factors
Already identified in the sector or activity.
PROOF
Seasonal recalibration in production.
SANY is our reference for seasonal recalibration.
Season-specific models after a winter decay
Models built in summer decayed in winter because the thermal baseline changed. The Drift Report made the shift visible, and engineers adapted and deployed season-specific models.
FAQ
Common questions.
Do we need several years of data to identify a reliable seasonal effect?
A minimum number of comparable cycles is needed to tell a recurring seasonal effect from a one-off variation. The exact number depends on the stability of the phenomenon observed.
Does recalibration replace a full retraining?
In most cases of purely seasonal drift, an adjustment can be enough. A full retraining remains necessary if the drift turns out to be structural rather than cyclical.
Does it apply to all types of models?
It applies to any model whose modeled behavior follows a predictable cycle, such as demand forecasting, commercial scoring or other use cases sensitive to seasonality.
How do we know if a drop in performance is seasonal?
Comparing the current period with the equivalent periods of previous cycles helps to tell the two apart. Modeler’s Drift Report shows how performance moves over time.
RELATED USE CASES
Keep exploring.
Model Drift Monitoring
See when a predictive model loses accuracy and needs an update.
Demand Forecasting
Estimate future demand per product and see what drives the forecast.
Sales Forecasting
Forecast revenue from the sales pipeline and past conversion, with the factors explained.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
