Feature selection for a real traffic data set"

In fsMTS: Feature Selection for Multivariate Time Series

knitr::opts_chunk$set(fig.width=6, fig.height=6)

require(fsMTS)
require(plot.matrix)
require(svMisc)
require(MTS)

data(traffic)
data <- scale(traffic$data[,-1])
max.lag <- 3
show.progress = F

mIndep<-fsMTS(data, max.lag=max.lag, method="ownlags",show.progress=show.progress)
plot(mIndep, col=rev(heat.colors(10)), key=NULL, 
     main="Only own lags")

mCCF<-fsMTS(data, max.lag=max.lag, method="CCF",show.progress=show.progress)
plot(mCCF, col=rev(heat.colors(10)), key=NULL, 
     main="Cross-correlations")

mDistance<-fsMTS(data, max.lag=max.lag, method="distance", shortest = traffic$shortest, step = 5,show.progress=show.progress)
plot(mDistance, col=rev(heat.colors(10)), key=NULL, 
     main="Distance-based feature selection")

mGLASSO.global<-fsMTS(data, max.lag=max.lag,method="GLASSO", rho = 0.1,show.progress=show.progress, localized = FALSE)
plot(mGLASSO.global, col=rev(heat.colors(10)), key=NULL, 
     main="Graphical LASSO-based feature selection")

mLARS<-fsMTS(data, max.lag=max.lag,method="LARS",show.progress=show.progress)
plot(mLARS, col=rev(heat.colors(10)), key=NULL, 
     main="Least angle  regression-based feature selection")

mRF.global<-fsMTS(data, max.lag=max.lag,method="RF",show.progress=show.progress, localized = FALSE)
plot(mRF.global, col=rev(heat.colors(10)), key=NULL, 
     main="Random forest-based (global) feature selection")

mMI.global<-fsMTS(data, max.lag=max.lag,method="MI",show.progress=show.progress, localized= FALSE)
plot(mMI.global, col=rev(heat.colors(10)), key=NULL, 
     main="Mutual information-based (global) feature selection")

mPSC<-fsMTS(data, max.lag=max.lag,method="PSC",show.progress=show.progress)
plot(mPSC, col=rev(heat.colors(10)), key=NULL, 
     main="PSC-based feature selection")

mlist <- list(Independent = mIndep,
              Distance = mDistance,
              CCF = mCCF,
              GLASSO.global = mGLASSO.global,
              LARS = mLARS,
              RF.global = mRF.global,
              MI.global = mMI.global,
              PSC=mPSC)

th<-0.1
mE1 <- fsEnsemble(mlist, threshold = th, method="ranking")
plot(mE1, col=rev(heat.colors(10)), key=NULL, 
     main="Ensemble feature selection  using Ranking")
mlist[["EnsembleRank"]] <- mE1


mE2 <- fsEnsemble(mlist, threshold = th, method="majority")
plot(mE2, col=rev(heat.colors(10)), key=NULL, 
     main="Ensemble feature selection  using Majority Voting")
mlist[["EnsembleMajV"]] <- mE2

msimilarity <- fsSimilarityMatrix(mlist, threshold=th, method="Kuncheva")
plot(msimilarity, digits=2, col=rev(heat.colors(ncol(msimilarity))), key=NULL,
     main="Pairwise comparison of feature sets", cex.axis=0.7)

Use obtained features for model estimation and prediction

model <- VAR(data, p=max.lag, include.mean = F, fixed = mE2)
print(model$coef)
VARpred(model,1)

fsMTS documentation built on April 26, 2022, 9:05 a.m.