diff --git a/tensorflow_probability/python/distributions/hidden_markov_model.py b/tensorflow_probability/python/distributions/hidden_markov_model.py
index 4426801375..bc30c5a8d8 100644
--- a/tensorflow_probability/python/distributions/hidden_markov_model.py
+++ b/tensorflow_probability/python/distributions/hidden_markov_model.py
@@ -1181,6 +1181,36 @@ def _reduce_one_step():
         return ps.cond(self.num_steps > 1, _reduce_multiple_steps,
                        _reduce_one_step)
 
+  def single_step_prediction(self, observation, prediction_distribution=None):
+      """
+      Function to run single prediction step based on incoming observation data point and the current prediction
+      distribution of the hmm model.
+      If no prediction_distribution is given (typically in the initial step), then the current distribution is derived
+      from the priors of the hmm (the initial_distribution over the states). In a forecasting
+      model that runs on live data, the first step would require initialisation while subsequent steps would use the
+      previous step's prediction distribution as input.
+      The prediction distribution is updated and returned from this function.
+      """
+      observation = tf.convert_to_tensor(observation, name='observations')
+
+      if prediction_distribution is None:
+          num = self.initial_distribution.log_prob(range(self.num_states_static)) \
+                + self.observation_distribution.log_prob(observation)
+      else:
+          if not isinstance(prediction_distribution, distribution.Distribution):
+              raise TypeError('If prediction_distribution is provided, it must be a Distribution object, '
+                              'but saw: %s' % prediction_distribution)
+          num = tf.math.log(prediction_distribution.probs) \
+                + self.observation_distribution.log_prob(observation)
+
+      filtering_distribution = tf.exp(num - tf.reduce_logsumexp(num))
+      prediction_distribution = tf.tensordot(self.transition_distribution.probs_parameter(),
+                                             filtering_distribution, axes=1)
+      prediction_distribution = categorical.Categorical(probs=prediction_distribution)
+      observation_prediction = tf.tensordot(prediction_distribution.probs, self.observation_distribution.mean(), axes=1)
+
+      return prediction_distribution, observation_prediction
+
   # pylint: disable=protected-access
   def _default_event_space_bijector(self):
     return (self._observation_distribution.