Oracle International Corporation

Nikan Chavoshi

Nipun Agarwal

Venkatanathan Varadarajan

Sandeep Agrawal

Hesam Fathi Moghadam

Sanjay Jinturkar

Anatoly Yakovlev

Ali Moharrer

Jingxiao Cai

Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.

Automated machine learning pipeline for timeseries datasets utilizing point-based algorithms

Growing community of inventors

Redwood City, CA, United States of America

Nikan Chavoshi