Explain weather-sensitive demand
How much of our sales pattern moves with weather?
The finished answer: A joined analysis dataset and an evaluated demand model—not a causal claim from weather alone.
Weather ingredients
These catalog parameters are part of the recipe. Dataset products such as warning geometry or gauge stage are linked below too.
A daily weather feature for comparing store conditions.
Definition, units and delivery references →Total Precipitation LWEWater-equivalent accumulation over the matching period.
Definition, units and delivery references →Total Heating Degree DaysA temperature-based accumulated demand feature, using the source’s base definition.
Definition, units and delivery references →Total Cooling Degree DaysA separate warm-weather demand feature.
Definition, units and delivery references →Ingredients you bring
- Sales/demand history by location and date
- Prices, promotions, holidays and opening hours
- Training/evaluation periods and business objective
These are not supplied automatically by a weather API.
The method
- Retrieve historical weather for the business locations.
- Align calendar/meteorological day definitions before joining.
- Add business controls and train the chosen statistical model.
- Evaluate on held-out periods before applying forecast inputs.
Enterprise/Data Suite history for the analysis; GIS for communicating geographic patterns.
Enterprise
Historical JSON/CSV is suited to joining weather with business records in a data pipeline.
GIS or a hybrid
Map the resulting store-level analysis using your own site layer. A current observation layer does not replace history.
Explore the actual dataset inputs
Open a product to inspect its guide or run its connected example. These demonstrate the ingredient—not a completed implementation of this recipe.