Amazon Technologies, Inc.

Mohammad Taha Bahadori

David Earl Heckerman

Zachary Chase Lipton

Techniques for generating multiple-resolutions of time series data are described. An input irregular time series having a plurality of data points is obtained, each data point of the plurality of data points including a timestamp and a feature vector. Based on the input irregular time series, multiple variant time series are generated. A data point in one of the variant time series is based in part on a combination of at least two data points of the input irregular time series. The multiple variant time series can then be used for machine learning tasks such as training a machine learning model or using a machine learning model to infer an output.

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Seattle, WA, United States of America

Mohammad Taha Bahadori