Time Series Simulator
- class tsbootstrap.time_series_simulator.TimeSeriesSimulator(fitted_model, X_fitted: ndarray, rng=None)[source]
Class to simulate various types of time series models.
- n_samples
Number of samples in the fitted time series model.
- Type:
int
- n_features
Number of features in the fitted time series model.
- Type:
int
- burnin
Number of burn-in samples to discard for certain models.
- Type:
int
- _validate_ar_simulation_params(params)[source]
Validate the parameters necessary for the simulation.
- _simulate_ar_residuals(lags, coefs, init, max_lag)[source]
Simulates an Autoregressive (AR) process with given lags, coefficients, initial values, and random errors.
- simulate_ar_process(resids_lags, resids_coefs, resids)[source]
Simulate AR process from the fitted model.
- _simulate_non_ar_residuals()[source]
Simulate residuals according to the model type.
- simulate_non_ar_process()[source]
Simulate a time series from the fitted model.
- generate_samples_sieve(model_type, resids_lags, resids_coefs, resids)[source]
Generate a bootstrap sample using the sieve bootstrap.
- property X_fitted: ndarray
Get the array of fitted values.
- property fitted_model
Get the fitted model.
- generate_samples_sieve(model_type: Literal['ar', 'arima', 'sarima', 'var', 'arch'], resids_lags: Integral | List[Integral] | None = None, resids_coefs: ndarray | None = None, resids: ndarray | None = None) ndarray[source]
Generate a bootstrap sample using the sieve bootstrap.
- Parameters:
model_type (ModelTypes) – The model type used for the simulation.
resids_lags (Optional[Union[Integral, List[Integral]]], optional) – The lags to be used in the AR process. Can be non-consecutive.
resids_coefs (Optional[np.ndarray], optional) – The coefficients corresponding to each lag. Of shape (1, len(lags)).
resids (Optional[np.ndarray], optional) – The initial values for the simulation. Should be at least as long as the maximum lag.
- Returns:
The bootstrap sample.
- Return type:
np.ndarray
- Raises:
ValueError – If resids_lags, resids_coefs, or resids are not provided.
- property rng
Get the random number generator instance.
- simulate_ar_process(resids_lags: Integral | List[Integral], resids_coefs: ndarray, resids: ndarray) ndarray[source]
Simulate AR process from the fitted model.
- Parameters:
resids_lags (Union[Integral, List[Integral]]) – The lags to be used in the AR process. Can be non-consecutive, but when called from generate_samples_sieve, it will be sorted.
resids_coefs (np.ndarray) – The coefficients corresponding to each lag. Of shape (1, len(lags)). Sorted by generate_samples_sieve corresponding to the sorted lags.
resids (np.ndarray) – The initial values for the simulation. Should be at least as long as the maximum lag.
- Returns:
The simulated AR process as a 1D NumPy array.
- Return type:
np.ndarray
- Raises:
ValueError – If resids_lags, resids_coefs, or resids are not provided. If resids_coefs is not a 1D NumPy array. If resids_coefs is not the same length as resids_lags. If resids is not the same length as X_fitted.
TypeError – If fitted_model is not an instance of AutoRegResultsWrapper. If resids_lags is not an integer or a list of integers.