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Framework for Bayesian structural time series models.
See the blog post for an introductory example.
Classes
class AdditiveStateSpaceModel
: A state space model representing a sum of component state space models.
class Autoregressive
: Formal representation of an autoregressive model.
class AutoregressiveIntegratedMovingAverage
: Represents an autoregressive integrated moving-average (ARIMA) model.
class AutoregressiveMovingAverageStateSpaceModel
: State space model for an autoregressive moving average process.
class AutoregressiveStateSpaceModel
: State space model for an autoregressive process.
class ConstrainedSeasonalStateSpaceModel
: Seasonal state space model with effects constrained to sum to zero.
class DynamicLinearRegression
: Formal representation of a dynamic linear regresson model.
class DynamicLinearRegressionStateSpaceModel
: State space model for a dynamic linear regression from provided covariates.
class IntegratedStateSpaceModel
: Integrates (/cumsums) a noise-free state space model.
class LinearRegression
: Formal representation of a linear regression from provided covariates.
class LocalLevel
: Formal representation of a local level model.
class LocalLevelStateSpaceModel
: State space model for a local level.
class LocalLinearTrend
: Formal representation of a local linear trend model.
class LocalLinearTrendStateSpaceModel
: State space model for a local linear trend.
class MaskedTimeSeries
: Named tuple encoding a time series Tensor
and optional missingness mask.
class MissingValuesTolerance
: MissingValuesTolerance(overall_fraction, fraction_low_missing_number, fraction_high_missing_number, low_missing_number, high_missing_number)
class Seasonal
: Formal representation of a seasonal effect model.
class SeasonalStateSpaceModel
: State space model for a seasonal effect.
class SemiLocalLinearTrend
: Formal representation of a semi-local linear trend model.
class SemiLocalLinearTrendStateSpaceModel
: State space model for a semi-local linear trend.
class SmoothSeasonal
: Formal representation of a smooth seasonal effect model.
class SmoothSeasonalStateSpaceModel
: State space model for a smooth seasonal effect.
class SparseLinearRegression
: Formal representation of a sparse linear regression.
class StructuralTimeSeries
: Base class for structural time series models.
class Sum
: Sum of structural time series components.
Functions
build_factored_surrogate_posterior(...)
: Build a variational posterior that factors over model parameters.
build_factored_surrogate_posterior_stateless(...)
: Returns stateless functions for building a variational posterior.
decompose_by_component(...)
: Decompose an observed time series into contributions from each component.
decompose_forecast_by_component(...)
: Decompose a forecast distribution into contributions from each component.
fit_with_hmc(...)
: Draw posterior samples using Hamiltonian Monte Carlo (HMC).
forecast(...)
: Construct predictive distribution over future observations.
impute_missing_values(...)
: Runs posterior inference to impute the missing values in a time series.
moments_of_masked_time_series(...)
: Compute mean and variance, accounting for a mask.
one_step_predictive(...)
: Compute one-step-ahead predictive distributions for all timesteps.
regularize_series(...)
: Infers frequency and makes an irregular time series regular.
sample_uniform_initial_state(...)
: Initialize from a uniform [-2, 2] distribution in unconstrained space.