TRMF (version 0.1.5)

TRMF_seasonal: Add seasonal regularization model to a TRMF object

Description

Creates a regularization scheme that favors seasonally varying solutions and adds it to a TRMF object. In matrix optimization form, it adds the following term to the TRMF cost function: \(R(x) = lambdaD^2||w(DX_s)||^2 + lambdaA^2||X_s||^2\) where \(X_s\) is sub-set of the Xm matrix controlled by this model and D is a (with a lag of freq) finite difference matrix.

Usage

TRMF_seasonal(obj,numTS = 1,freq = 12,sumFirst=FALSE,lambdaD=1,lambdaA=0.0001,weight=1)

Arguments

obj

A TRMF object

numTS

number of latent time series in this model

lambdaD

regularization parameter for temporal constraint matrix

lambdaA

regularization parameter to apply simple L2 regularization to this time series model

weight

optional vector of weights to weight constraints, i.e. R(x) = lambdaD^2*||w*(D%*%X)||^2

freq

The frequency of the seasonal time series model. Minimize the differences of lag = freq

sumFirst

minimize the sum of first freq elements in time series

Value

Returns an updated object of class TRMF.

Details

TRMF_seasonal(freq=N) fits a lag N random walk. For monthly data, use freq=12, for quarterly data, freq=4. If sumFirst = TRUE, the sum of the first freq elements in the latent time series are also minimized.This can be used to help force the seasonal component to vary around a zero mean.

References

Yu, Hsiang-Fu, Nikhil Rao, and Inderjit S. Dhillon. "High-dimensional time series prediction with missing values." arXiv preprint arXiv:1509.08333 (2015).

See Also

create_TRMF, TRMF_columns,TRMF_simple, TRMF_trend

Examples

# NOT RUN {
# create test data
tm = 3*poly(x = (-20:20)/10,degree=3)
sm = diffinv(rnorm(29,0,.1),lag=12,xi=(-5:6)/6)
xm = cbind(sm,tm)
fm = matrix(runif(40),4,10)
Am = xm%*%fm+rnorm(410,0,.1)

# create model
obj = create_TRMF(Am)
obj = TRMF_columns(obj,reg_type ="interval")
obj = TRMF_trend(obj,numTS=3,order=2)
obj = TRMF_seasonal(obj,numTS=1,freq=12,lambdaD=5)
out = train(obj)
plot(out)

# }