Datarobot, Inc.

John Bledsoe

Ryan West

Jason Montgomery

Matt Steinpreis

Craig Vermeer

Jeff Gabriel

Ryan Sevey

An apparatus has a memory with processor-executable instructions and a processor operatively coupled to the memory. The apparatus receives datasets including time series data points that are descriptive of a feature of a given entity. The processor determines a time series characteristic based on the data content, and selects, based on the determined characteristic, a set of entrant forecasting models from a pool of forecasting models stored in the memory. Next, the processor trains each entrant forecasting model with the time series data points to produce a set of trained entrant forecasting models. The processor executes each trained entrant forecasting model to generate a set of forecasted values indicating estimations of the feature of the given entity. Thereafter the processor selects at least one forecasting model from the set of trained entrant forecasting models based on computed accuracy evaluations performed over the set of forecasted values.

Methods for self-adaptive time series forecasting, and related systems and apparatus

Methods and apparatus for self-adaptive time series forecasting engine

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Columbus, OH, United States of America

John Bledsoe