Planalytics, Inc.

Michael A Willen

Paul E Walsh

David G Estornell

Frederic D Fox

William E Kirk

Mike Kowa

Eli H Miller

Doug Pearson

Weather metrics data and a weather factor relationship knowledgebase are received and used to forecast the weather-based demand. The weather factor relationship knowledgebase can be a weather-impact model. The weather-impact model can comprise at least one of an empirical scoring matrix, a weather indices template, and a proxy model conditions template. The weather-impact model can be derived from an analysis of normalized proxy sales history data. Optionally, the weather-based demand can be scaled, deaggregated, or both.

System, method, and computer program product for forecasting weather-based demand using proxy data

Systems and methods to generate business recommendations for specific business actions based on weather element forecasts and known relationships between a business activity and weather elements are provided. The system includes a confidence level filter, an opportunity matrix filter, a weather decision point generator, a business rule recommendation engine and a business rule knowledge database. Methods to generate a business recommendation for a business activity are also provided. The methods include receiving weather driven demand data, assigning opportunity measures to each of the data points, identifying weather decision points based on opportunity measures, and applying business weather rules to the weather decision points to generate business recommendations. In a further feature, a weather element relationship and/or a weather element forecast confidence level is assigned to each data point within the weather driven demand data. These confidence levels are then factored in to determine weather decision points.

Systems and methods for recommending business decisions utilizing weather driven demand data and opportunity and confidence measures

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Thorndale, PA, United States of America

Michael A Willen