from pyro.nn import PyroModule, pyro_method
[docs]class TimeSeriesModel(PyroModule):
"""
Base class for univariate and multivariate time series models.
"""
[docs] @pyro_method
def log_prob(self, targets):
"""
Log probability function.
:param torch.Tensor targets: A 2-dimensional tensor of real-valued targets
of shape ``(T, obs_dim)``, where ``T`` is the length of the time series and ``obs_dim``
is the dimension of the real-valued ``targets`` at each time step
:returns torch.Tensor: A 0-dimensional log probability for the case of properly
multivariate time series models in which the output dimensions are correlated;
otherwise returns a 1-dimensional tensor of log probabilities for batched
univariate time series models.
"""
raise NotImplementedError
[docs] @pyro_method
def forecast(self, targets, dts):
"""
:param torch.Tensor targets: A 2-dimensional tensor of real-valued targets
of shape ``(T, obs_dim)``, where ``T`` is the length of the time series and ``obs_dim``
is the dimension of the real-valued targets at each time step. These
represent the training data that are conditioned on for the purpose of making
forecasts.
:param torch.Tensor dts: A 1-dimensional tensor of times to forecast into the future,
with zero corresponding to the time of the final target ``targets[-1]``.
:returns torch.distributions.Distribution: Returns a predictive distribution with batch shape ``(S,)`` and
event shape ``(obs_dim,)``, where ``S`` is the size of ``dts``. That is, the resulting
predictive distributions do not encode correlations between distinct times in ``dts``.
"""
raise NotImplementedError