tf.contrib.timeseries.predict_continuation_input_fn

tf.contrib.timeseries.predict_continuation_input_fn(
    evaluation,
    steps=None,
    times=None,
    exogenous_features=None
)

Defined in tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py.

An Estimator input_fn for running predict() after evaluate().

If the call to evaluate() we are making predictions based on had a batch_size greater than one, predictions will start after each of these windows (i.e. will have the same batch dimension).

Args:

  • evaluation: The dictionary returned by Estimator.evaluate, with keys FilteringResults.STATE_TUPLE and FilteringResults.TIMES.
  • steps: The number of steps to predict (scalar), starting after the evaluation. If times is specified, steps must not be; one is required.
  • times: A [batch_size x window_size] array of integers (not a Tensor) indicating times to make predictions for. These times must be after the corresponding evaluation. If steps is specified, times must not be; one is required. If the batch dimension is omitted, it is assumed to be 1.
  • exogenous_features: Optional dictionary. If specified, indicates exogenous features for the model to use while making the predictions. Values must have shape [batch_size x window_size x ...], where batch_size matches the batch dimension used when creating evaluation, and window_size is either the steps argument or the window_size of the times argument (depending on which was specified).

Returns:

An input_fn suitable for passing to the predict function of a time series Estimator.

Raises:

  • ValueError: If times or steps are misspecified.