scalation
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scalation/scalation/scalation.modeling/scalation.modeling.forecasting_old/KalmanFilter

KalmanFilter

scalation.modeling.forecasting_old.KalmanFilter
class KalmanFilter(x0: VectorD, ff: MatrixD, hh: MatrixD, qq: MatrixD, rr: MatrixD, gg: MatrixD, u: VectorD)

The KalmanFilter class is used to fit state-space models. x_t = F x_t-1 + G u_t + w_t (State Equation) z_t = H x_t + v_t (Observation/Measurement Equation)

Value parameters

ff

the state transition matrix (F)

gg

the optional control-input matrix (G)

hh

the observation matrix (H)

qq

the process noise covariance matrix (Q)

rr

the observation noise covariance matrix (R)

u

the optional control vector

x0

the initial state vector

Attributes

Graph
Supertypes
class Object
trait Matchable
class Any

Members list

Value members

Concrete methods

def predict(): Unit

Predict the state of the process at the next time point.

Predict the state of the process at the next time point.

Attributes

def solve(dt: Double, u: VectorD): VectorD

Iteratively solve for x using predict and update phases.

Iteratively solve for x using predict and update phases.

Value parameters

dt

the time increment (delta t)

u

the control vector

Attributes

def update(z: VectorD): Unit

Update the state and covariance estimates with the current and possibly noisy measurements

Update the state and covariance estimates with the current and possibly noisy measurements

Value parameters

z

current measurement/observation of the state

Attributes

Concrete fields

val traj: MatrixD
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