Defined in tensorflow/contrib/timeseries/python/timeseries/

Perform prediction using an exported saved model.

Analogous to _input_pipeline.predict_continuation_input_fn, but operates on a saved model rather than feeding into Estimator's predict method.


  • continue_from: A dictionary containing the results of either an Estimator's evaluate method or filter_continuation. Used to determine the model state to make predictions starting from.
  • signatures: The MetaGraphDef protocol buffer returned from tf.saved_model.loader.load. Used to determine the names of Tensors to feed and fetch. Must be from the same model as continue_from.
  • session: The session to use. The session's graph must be the one into which tf.saved_model.loader.load loaded the model.
  • steps: The number of steps to predict (scalar), starting after the evaluation or filtering. 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 or filtering. 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 continue_from, and window_size is either the steps argument or the window_size of the times argument (depending on which was specified).


A dictionary with model-specific predictions (typically having keys "mean" and "covariance") and a feature_keys.PredictionResults.TIMES key indicating the times for which the predictions were computed.


  • ValueError: If times or steps are misspecified.