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STAGED TRAINING OF NEURAL NETWORKS FOR IMPROVED TIME SERIES PREDICTION PERFORMANCE

  • US 20200133977A1
  • Filed: 12/26/2019
  • Published: 04/30/2020
  • Est. Priority Date: 02/05/2016
  • Status: Active Grant
First Claim
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1. An apparatus comprising a processor and a storage to store instructions that, when executed by the processor, cause the processor to perform operations comprising:

  • train a first neural network of a chain of neural networks to generate a first portion of multiple portions of time series data that corresponds to a temporally earliest subrange of time of multiple subranges of time within a full range of time that is covered by the time series data, wherein;

    the chain comprises a set of neural networks ordered to start with the first neural network at a head of the chain and to end with a last neural network at a tail of the chain;

    each neural network of the chain comprises external inputs, additional inputs and outputs;

    each neural network of the chain generates a portion of the multiple portions of the time series data at the outputs of the neural network from input data values provided at the external inputs of the neural network;

    each portion of the multiple portions of the time series data corresponds to a subrange of the multiple subranges; and

    the set of neural networks is interconnected within the chain such that each neural network, except the first neural network at the head of the chain, receives, at the additional inputs of the neural network, a portion of the multiple portions of the time series data that is generated at the outputs of a preceding neural network in the ordering of neural networks within the chain;

    retrieve, from the first neural network, a first neural network configuration data comprising hyperparameters and first trained parameters learned by the first neural network from the training of the first neural network;

    train, using at least the first neural network configuration data, a next neural network in the ordering of neural networks within the chain to generate a next portion of the multiple portions that corresponds to a next subrange of time of the multiple subranges of time that temporally follows the earliest subrange;

    retrieve, from the next neural network, a next neural network configuration data comprising the hyperparameters and next trained parameters learned by the next neural network from the training of the next neural network; and

    use at least the first neural network configuration data and the next neural network configuration data to instantiate the chain.

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