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    Probabilistic forecasting with nonparametric quantile functions
    1.
    发明授权
    Probabilistic forecasting with nonparametric quantile functions 有权

    公开(公告)号：US11531917B1

    公开(公告)日：2022-12-20

    申请号：US16147147

    申请日：2018-09-28

    Applicant: Amazon Technologies, Inc.

    Inventor： Jan Gasthaus ,  Konstantinos Benidis ,  Yuyang Wang ,  David Salinas ,  Valentin Flunkert

    IPC: G06N3/08 ,  G06N7/00

    Abstract: Techniques are described for a time series probabilistic forecasting framework that combines recurrent neural networks (RNNs) with a flexible, nonparametric representation of the output distribution. The representation is based on the nonparametric quantile function (instead of, for example, a parametric density function) and is trained by minimizing a continuous ranked probability score (CRPS) derived from the quantile function. Unlike methods based on parametric probability density functions and maximum likelihood estimation, the techniques described herein can flexibly adapt to different output distributions without manual intervention. Furthermore, the nonparametric nature of the quantile function provides a significant boost in the approach's robustness, making it more readily applicable to a wide variety of time series datasets.

    Neural contrastive anomaly detection
    7.
    发明授权
    Neural contrastive anomaly detection 有权

    公开(公告)号：US11636125B1

    公开(公告)日：2023-04-25

    申请号：US17364212

    申请日：2021-06-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Christian Uriel Carmona Perez ,  Francois-Xavier Benoit Marie Aubet ,  Valentin Flunkert ,  Jan Gasthaus

    IPC: G06F16/2458 ,  G06N3/08

    Abstract: Systems and methods are described for detecting anomalies within data, such as time series data. In one example, unlabeled data, such as time series data, may be obtained. At least one data point, representing an artificial anomaly, may be inserted into the data. The data may then be divided into a number of different windows. The windows may have a fixed size and may at least partially overlap in time. The data contained within different windows may be compared, to each other and to the injected data point, to determine an anomaly score for individual windows. The anomaly score may indicate a likelihood that a given window contains an anomaly. In a specific example, a convolution neural network may be trained based on the data and inserted data points representing anomalies, where a contrastive loss function is used to represent different portions of the data in the neural network.

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