| TRMF_es | R Documentation |
Creates a regularization scheme that favors exponentially smoothed 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 matrix with weights from exponential smoothing.
TRMF_es(obj,numTS = 1,alpha=1,es_type=c("single","double"),
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 constraint, i.e. R(x) = lambdaD^2*||w*(D%*%X)||^2 |
es_type |
type of exponential smoothing. |
alpha |
exponential smoothing parameter, constrained to be in the interval [0,1] |
This creates a non-sparse constraint matrix which could slow training down for longer time series.
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).
https://en.wikipedia.org/wiki/Exponential_smoothing
create_TRMF, TRMF_columns, TRMF_trend,TRMF_seasonal
# create test data xm = cbind(cumsum(rnorm(20)),cumsum(rnorm(20))) fm = matrix(runif(20),2,10) Am = xm%*%fm+rnorm(200,0,.2) # create model obj = create_TRMF(Am) obj = TRMF_es(obj,numTS=2,alpha=0.5) out = train(obj) plot(out)