An Estimator input_fn for running predict() after evaluate().
tf.contrib.timeseries.predict_continuation_input_fn( evaluation, steps=None, times=None, exogenous_features=None )
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).
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
stepsmust 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
timesmust 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_sizematches the batch dimension used when creating
window_sizeis either the
stepsargument or the
timesargument (depending on which was specified).
input_fn suitable for passing to the
predict function of a time