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KalmanFilterTransformerSIMD

API referenceSource
MultivariateMissing values

Vectorized Kalman Filter from simdkalman.

Quickstart

python
from sktime.transformations.kalman_filter import KalmanFilterTransformerSIMD

estimator = KalmanFilterTransformerSIMD(state_dim, state_transition=None, process_noise=None, measurement_noise=None, measurement_function=None, initial_state=None, initial_state_covariance=None, denoising=False, hidden=True)

Parameters(9)

state_dimint
System state feature dimension.
state_transitionnp.ndarray, optional (default=None)

of shape (state_dim, state_dim). State transition matrix, also referred to as F, is a matrix which describes the way the underlying series moves through successive time periods. Called A in simdkalman.

process_noisenp.ndarray, optional (default=None)

of shape (state_dim, state_dim). Process noise matrix, also referred to as Q, the uncertainty of the dynamic model.

measurement_noisenp.ndarray, optional (default=None)

of shape (measurement_dim, measurement_dim). Measurement noise matrix, also referred to as R, represents the uncertainty of the measurements.

measurement_functionnp.ndarray, optional (default=None)

of shape (measurement_dim, state_dim). Measurement equation matrix, also referred to as H, adjusts dimensions of measurements to match dimensions of state.

initial_statenp.ndarray, optional (default=None)

of shape (state_dim,). Initial estimated system state, also referred to as X0.

initial_state_covariancenp.ndarray, optional (default=None)

of shape (state_dim, state_dim). Initial estimated system state covariance, also referred to as P0.

denoisingbool, optional (default=False).

This parameter affects transform. If False, then transform will use a Kalman filter (forward pass only). If true, uses a Kalman smoother.

hiddenbool, optional (default=True).

This parameter affects transform. If True, then transform will be inferring hidden state. If False, returns smoothed/filtered observations (see also denoising), which always has the same dimensions as the input data, independent of the hidden state dimension.