Microsoft Technology Licensing, LLC

Dhawal Dilip Parkar

Neil Tenenholtz

Vijaykumar K Aski

Nazmiye Ceren Abay

Nikolay Sergeyevich Rovinskiy

The disclosed techniques pertain to the dynamic control of select functions that are applied to a time series dataset based on the detection of stationary time series grains. In some configurations, a system selectively applies select functions, e.g., the application of a differencing function, to a dataset in response to determining that a number of stationary time series grains detected in the dataset meets one or more criteria with respect to a threshold. If a system determines that the number of stationary time series grains meets one or more criteria with respect to a threshold, the system can apply a differencing function to the entire dataset. By controlling the differencing function based on the detection of stationary time series grains with respect to a threshold, the system can increase the accuracy and the efficiency of a machine learning system or any other system that utilizes time series datasets.

Growing community of inventors

Bellevue, WA, United States of America

Dhawal Dilip Parkar