Systems and methods for learning and predicting time-series data using inertial auto-encoders
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United States of America Patent
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Nov 17, 2020
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Feb 8, 2018
app pub date -
Aug 1, 2017
filing date -
Aug 8, 2016
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Abstract
A method includes using a computational network to learn and predict time-series data. The computational network is configured to receive the time-series data and perform transformation-invariant encoding of the time-series data. The computational network includes one or more encoding layers. The method also includes feeding back future predictions of the time-series data through inertial adjustments of transformations. The inertial adjustments preserve the invariants in the computational network. The computational network could further include one or more pooling layers each configured to reduce dimensionality of data, where the one or more pooling layers provide the transformation invariance for the encoding.
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