LostTech.TensorFlow : API Documentation

Type ARRegressor

Namespace tensorflow.contrib.timeseries

Parent TimeSeriesRegressor

Interfaces IARRegressor

An Estimator for an (optionally non-linear) autoregressive model.

ARRegressor is a window-based model, inputting fixed windows of length `input_window_size` and outputting fixed windows of length `output_window_size`. These two parameters must add up to the window_size passed to the `Chunker` used to create an `input_fn` for training or evaluation. `RandomWindowInputFn` is suggested for both training and evaluation, although it may be seeded for deterministic evaluation.



Public static methods

ARRegressor NewDyn(object periodicities, object input_window_size, object output_window_size, object num_features, object exogenous_feature_columns, ImplicitContainer<T> num_time_buckets, ImplicitContainer<T> loss, object hidden_layer_sizes, object anomaly_prior_probability, object anomaly_distribution, object optimizer, object model_dir, object config)

Initialize the Estimator.
object periodicities
periodicities of the input data, in the same units as the time feature. Note this can be a single value or a list of values for multiple periodicities.
object input_window_size
Number of past time steps of data to look at when doing the regression.
object output_window_size
Number of future time steps to predict. Note that setting it to > 1 empirically seems to give a better fit.
object num_features
The dimensionality of the time series (one for univariate, more than one for multivariate).
object exogenous_feature_columns
A list of tf.feature_columns (for example tf.feature_column.embedding_column) corresponding to exogenous features which provide extra information to the model but are not part of the series to be predicted. Passed to `tf.compat.v1.feature_column.input_layer`.
ImplicitContainer<T> num_time_buckets
Number of buckets into which to divide (time % periodicity) for generating time based features.
ImplicitContainer<T> loss
Loss function to use for training. Currently supported values are SQUARED_LOSS and NORMAL_LIKELIHOOD_LOSS. Note that for NORMAL_LIKELIHOOD_LOSS, we train the covariance term as well. For SQUARED_LOSS, the evaluation loss is reported based on un-scaled observations and predictions, while the training loss is computed on normalized data.
object hidden_layer_sizes
list of sizes of hidden layers.
object anomaly_prior_probability
If specified, constructs a mixture model under which anomalies (modeled with `anomaly_distribution`) have this prior probability. See `AnomalyMixtureARModel`.
object anomaly_distribution
May not be specified unless anomaly_prior_probability is specified and is not None. Controls the distribution of anomalies under the mixture model. Currently either `ar_model.AnomalyMixtureARModel.GAUSSIAN_ANOMALY` or `ar_model.AnomalyMixtureARModel.CAUCHY_ANOMALY`. See `AnomalyMixtureARModel`. Defaults to `GAUSSIAN_ANOMALY`.
object optimizer
The optimization algorithm to use when training, inheriting from tf.train.Optimizer. Defaults to Adagrad with step size 0.1.
object model_dir
See `Estimator`.
object config
See `Estimator`.

Public properties

object config get;

object config_dyn get;

object model_dir get;

object model_dir_dyn get;

object model_fn get;

object model_fn_dyn get;

object params get;

object params_dyn get;

object PythonObject get;