| TRMF_ar | R Documentation |
Creates a regularization scheme that constrains latent time-series based on auto-regressive parameters 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 matrix that corresponds to an auto-regressive model.
TRMF_ar(obj,numTS = 1,AR,lambdaD=1,lambdaA=0.0001,weight=1)
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 |
AR |
vector of autoregressive parameters. No checks are performed |
Setting AR = c(1) gives a random walk model, same as TRMF_trend(..., order=1)
Returns an updated object of class TRMF.
Chad Hammerquist
Yu, Hsiang-Fu, Nikhil Rao, and Inderjit S. Dhillon. "High-dimensional time series prediction with missing values." arXiv preprint arXiv:1509.08333 (2015).
create_TRMF, TRMF_columns, TRMF_trend
# create test data xm = matrix(rnorm(80),20,4) fm = matrix(rnorm(40),4,10)+1 Am = xm%*%fm+rnorm(200,0,.1) # create model obj = create_TRMF(Am) obj = TRMF_columns(obj,reg_type ="interval") obj = TRMF_ar(obj,numTS=2,AR=c(0.5),lambdaD=4) out = train(obj) plot(out)