The Substantial Role of Weather Data in Consumer Spending Prediction: A Robust Machine Learning Assessment

isaac gerg, Arik M Tashie, Amiya Patanaik, Evan Koester, Himanshu Gupta, David J Farnham

Accurate forecasting of daily consumer spending is
crucial for strategic decision-making in the retail sector, yet the
dynamic influence of weather remains underutilized in predictive
models. Grounded in the Stimulus-Organism-Response frame-
work and demand theory, this study examines how weather acts
as an environmental stimulus triggering behavioral responses that
differentially affect spending across sectors of varying demand
elasticity. We present a comprehensive evaluation of weather
data integration for consumer spending prediction across three
retail sectors: grocers, home improvement, casual dining. We
employ a robust methodology involving eight distinct machine
learning models, from linear regression to ensemble methods.
Each is trained with and without weather features to isolate
meteorological contributions independent of algorithmic choice.
Our experimental framework encompasses 1.2 million individual
model training runs across all 50 US states over 10 years, evalu-
ating multiple scenarios ranging from operational forecasting to
theoretical performance bounds. Models incorporating weather
data achieve a mean symmetric Mean Absolute Percentage
Error (sMAPE) improvement of 11.5% compared to baselines
using only economic features, with some methods exhibiting
statistically significant gains in 74% of combinations across states
and industries. Performance gains vary systematically by sector,
with grocers achieving 20.2% improvement, casual dining 12.2%,
and home improvement 3.3%, reflecting differential weather
sensitivity across necessity versus discretionary goods, consistent
with demand theory predictions. These findings demonstrate
weather data’s substantial predictive value for consumer spend-
ing forecasting across diverse machine learning approaches and
geographic contexts, with sector-specific performance differences
reflecting underlying demand elasticity and weather-driven be-
havioral mechanisms predicted by economic theory.