tfp.sts.impute_missing_values

Runs posterior inference to impute the missing values in a time series. (deprecated argument values)

tfp.sts.impute_missing_values(
    model,
    observed_time_series,
    parameter_samples,
    include_observation_noise=False,
    timesteps_are_event_shape=True
)

This method computes the posterior marginals p(latent state | observations), given the time series at observed timesteps (a missingness mask should be specified using tfp.sts.MaskedTimeSeries). It pushes this posterior back through the observation model to impute a predictive distribution on the observed time series. At unobserved steps, this is an imputed value; at other steps it is interpreted as the model's estimate of the underlying noise-free series.

Args
`model`	`tfp.sts.Sum` instance defining an additive STS model.
`observed_time_series`	`float` `Tensor` of shape `concat([sample_shape, model.batch_shape, [num_timesteps, 1]])` where `sample_shape` corresponds to i.i.d. observations, and the trailing `[1]` dimension may (optionally) be omitted if `num_timesteps > 1`. Any `NaN`s are interpreted as missing observations; missingness may be also be explicitly specified by passing a `tfp.sts.MaskedTimeSeries` instance.
`parameter_samples`	Python `list` of `Tensors` representing posterior samples of model parameters, with shapes `[concat([ [num_posterior_draws], param.prior.batch_shape, param.prior.event_shape]) for param in model.parameters]`. This may optionally also be a map (Python `dict`) of parameter names to `Tensor` values.
`include_observation_noise`	If `False`, the imputed uncertainties represent the model's estimate of the noise-free time series at each timestep. If `True`, they represent the model's estimate of the range of values that could be observed at each timestep, including any i.i.d. observation noise. Default value: `False`.
`timesteps_are_event_shape`	Deprecated, for backwards compatibility only. If `False`, the predictive distribution will return per-timestep probabilities Default value: `True`.

Returns
`imputed_series_dist`	a `tfd.MixtureSameFamily` instance with event shape `[num_timesteps] if timesteps_are_event_shape else []` and batch shape `concat([sample_shape, model.batch_shape, [] if timesteps_are_event_shape else [num_timesteps])`, with `num_posterior_draws` mixture components.

Example

To specify a time series with missing values, use tfp.sts.MaskedTimeSeries:

time_series_with_nans = [-1., 1., np.nan, 2.4, np.nan, 5]
observed_time_series = tfp.sts.MaskedTimeSeries(
  time_series=time_series_with_nans,
  is_missing=tf.math.is_nan(time_series_with_nans))

Masked time series can be passed to tfp.sts methods in place of a observed_time_series Tensor:

# Build model using observed time series to set heuristic priors.
linear_trend_model = tfp.sts.LocalLinearTrend(
  observed_time_series=observed_time_series)
model = tfp.sts.Sum([linear_trend_model],
                    observed_time_series=observed_time_series)

# Fit model to data
parameter_samples, _ = tfp.sts.fit_with_hmc(model, observed_time_series)

After fitting a model, impute_missing_values will return a distribution

# Impute missing values
imputed_series_distribution = tfp.sts.impute_missing_values(
  model, observed_time_series, parameter_samples=parameter_samples)
print('imputed means and stddevs: ',
      imputed_series_distribution.mean(),
      imputed_series_distribution.stddev())

tfp.sts.impute_missing_values

Args

Returns

Example