Systems and methods for time series forecasting

A non-transitory machine-readable medium comprising a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method comprising:

• receiving, via an input interface, a time series dataset that includes datapoints at a plurality of timestamps in an observed space;
• training a neural network system using the time series dataset, wherein the training the neural network system includes:
• generating, using a first encoder of the neural network system, one or more estimated latent variables of a latent space for the time series dataset;
• generating, using an auxiliary predictor of the neural network system, a first latent-space prediction result based on the one or more estimated latent variables;
• transforming, using a first decoder of the neural network system, the first latent-space prediction result to a first observed-space prediction result;
• and
• updating parameters of the neural network system using a first objective based on the first latent-space prediction result and the first observed-space prediction result.

A system, comprising:

• a non-transitory memory;
• and
• one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform a method comprising:
• receiving, via an input interface, a time series dataset that includes datapoints at a plurality of timestamps in an observed space;
• training a neural network system using the time series dataset, wherein the training the neural network system includes:
• generating, using a first encoder of the neural network system, one or more estimated latent variables of a latent space for the time series dataset;
• generating, using an auxiliary predictor of the neural network system, a first latent-space prediction result based on the one or more estimated latent variables;
• transforming, using a first decoder of the neural network system, the first latent-space prediction result to a first observed-space prediction result;
• and
• updating parameters of the neural network system using a first objective based on the first latent-space prediction result and the first observed-space prediction result.