TimeSeriesSRC.pmodel

class TimeSeriesSRC.pmodel(xtype, na=[0, 1], nb=[], nc=[], nd=[], nf=[], delay=[], diff=[0], per=[], upre=[], ypre=[], ypost=[], eFcn='estimlm', indexFcn='pmodmse', initFcn='initrand')

Bases: object

__init__(xtype, na=[0, 1], nb=[], nc=[], nd=[], nf=[], delay=[], diff=[0], per=[], upre=[], ypre=[], ypost=[], eFcn='estimlm', indexFcn='pmodmse', initFcn='initrand')

Methods

__init__(xtype[, na, nb, nc, nd, nf, delay, ...])
getGH()
getGHarma()
getGHarmax()
getGHarx()
getGHbjtf()
getGHdf()
getGHdfarma()
getGHdfbjtf()
getmX()
getmXarma()
getmXarmax()
getmXarx()
getmXbjtf()
getmXregr()
init()
initrand()
initrandn()
initzero()
new_model()
newarma()
newarmax()
newarx()
newbjtf()
newregr()
predarma([y])
predarmax(y[, u])
predarx(y[, u])
predbjtf(y[, u])
preddfarma(y)
preddfbjtf(y, u)	PREDICT Compute one-step predictions for the Box and Jenkins Transfer Function model.
predict(y[, u])
predictdf(y[, u])
predregr(y[, u])	PREDICT Compute one-step predictions for regression model.
set_data(y[, u])
setmX(X)
setmXarma(X)
setmXarmax(X)
setmXarx(X)
setmXbjtf(X)
setmXregr(X)

set_data(y, u=array([], shape=(1, 0), dtype=float64))

new_model()

newregr()

newbjtf()

newarx()

newarmax()

newarma()

init()

initzero()

initrandn()

initrand()

getmX()

getmXregr()

getmXbjtf()

getmXarx()

getmXarmax()

getmXarma()

setmX(X)

setmXregr(X)

setmXbjtf(X)

setmXarx(X)

setmXarmax(X)

setmXarma(X)

predict(y, u=[])

predregr(y, u=array([], shape=(1, 0), dtype=float64))

PREDICT Compute one-step predictions for regression model.

Parameters:

• y

• u

Returns:

predbjtf(y, u=[])

predarx(y, u=array([], shape=(1, 0), dtype=float64))

predarmax(y, u=[[]])

predarma(y=[])

predictdf(y, u=[[]])

preddfbjtf(y, u)

PREDICT Compute one-step predictions for the Box and Jenkins Transfer Function model. :param y: :param u: :return:

preddfarma(y)

getGHdf()

getGHdfbjtf()

getGHdfarma()

getGH()

getGHbjtf()

getGHarx()

getGHarmax()

getGHarma()