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

Perform filtering using an exported saved model.

Filtering refers to updating model state based on new observations. Predictions based on the returned model state will be conditioned on these observations.


  • continue_from: A dictionary containing the results of either an Estimator's evaluate method or a previous filter step (cold start or continuation). Used to determine the model state to start filtering 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.
  • features: A dictionary mapping keys to Numpy arrays, with several possible shapes (requires keys FilteringFeatures.TIMES and FilteringFeatures.VALUES): Single example; TIMES is a scalar and VALUES is either a scalar or a vector of length [number of features]. Sequence; TIMES is a vector of shape [series length], VALUES either has shape series length or series length x number of features. Batch of sequences; TIMES is a vector of shape [batch size x series length], VALUES has shape [batch size x series length] or [batch size x series length x number of features]. In any case, VALUES and any exogenous features must have their shapes prefixed by the shape of the value corresponding to the TIMES key.


A dictionary containing model state updated to account for the observations in features.