Module: tf.contrib.timeseries
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A time series library in TensorFlow (TFTS).
Modules
saved_model_utils
module: Convenience functions for working with time series saved_models.
Classes
class ARModel
: Auto-regressive model, both linear and non-linear.
class ARRegressor
: An Estimator for an (optionally non-linear) autoregressive model.
class CSVReader
: Reads from a collection of CSV-formatted files.
class FilteringResults
: Keys returned from evaluation/filtering.
class NumpyReader
: A time series parser for feeding Numpy arrays to a TimeSeriesInputFn
.
class OneShotPredictionHead
: A time series head which exports a single stateless serving signature.
class RandomWindowInputFn
: Wraps a TimeSeriesReader
to create random batches of windows.
class StructuralEnsembleRegressor
: An Estimator for structural time series models.
class TimeSeriesRegressor
: An Estimator to fit and evaluate a time series model.
class TrainEvalFeatures
: Feature names used during training and evaluation.
class WholeDatasetInputFn
: Supports passing a full time series to a model for evaluation/inference.
Functions
predict_continuation_input_fn(...)
: An Estimator input_fn for running predict() after evaluate().
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Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# Module: tf.contrib.timeseries\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/__init__.py) |\n\nA time series library in TensorFlow (TFTS).\n\nModules\n-------\n\n[`saved_model_utils`](../../tf/contrib/timeseries/saved_model_utils) module: Convenience functions for working with time series saved_models.\n\nClasses\n-------\n\n[`class ARModel`](../../tf/contrib/timeseries/ARModel): Auto-regressive model, both linear and non-linear.\n\n[`class ARRegressor`](../../tf/contrib/timeseries/ARRegressor): An Estimator for an (optionally non-linear) autoregressive model.\n\n[`class CSVReader`](../../tf/contrib/timeseries/CSVReader): Reads from a collection of CSV-formatted files.\n\n[`class FilteringResults`](../../tf/contrib/timeseries/FilteringResults): Keys returned from evaluation/filtering.\n\n[`class NumpyReader`](../../tf/contrib/timeseries/NumpyReader): A time series parser for feeding Numpy arrays to a `TimeSeriesInputFn`.\n\n[`class OneShotPredictionHead`](../../tf/contrib/timeseries/OneShotPredictionHead): A time series head which exports a single stateless serving signature.\n\n[`class RandomWindowInputFn`](../../tf/contrib/timeseries/RandomWindowInputFn): Wraps a `TimeSeriesReader` to create random batches of windows.\n\n[`class StructuralEnsembleRegressor`](../../tf/contrib/timeseries/StructuralEnsembleRegressor): An Estimator for structural time series models.\n\n[`class TimeSeriesRegressor`](../../tf/contrib/timeseries/TimeSeriesRegressor): An Estimator to fit and evaluate a time series model.\n\n[`class TrainEvalFeatures`](../../tf/contrib/timeseries/TrainEvalFeatures): Feature names used during training and evaluation.\n\n[`class WholeDatasetInputFn`](../../tf/contrib/timeseries/WholeDatasetInputFn): Supports passing a full time series to a model for evaluation/inference.\n\nFunctions\n---------\n\n[`predict_continuation_input_fn(...)`](../../tf/contrib/timeseries/predict_continuation_input_fn): An Estimator input_fn for running predict() after evaluate()."]]