fsMTS | R Documentation |
fsMTS
implements algorithms for feature selection in multivariate time series
fsMTS( mts, max.lag, method = c("ownlags", "distance", "CCF", "MI", "RF", "GLASSO", "LARS", "PSC"), show.progress = F, localized = F, ... )
mts |
an matrix object with values of the multivariate time series (MTS) MTS components are by k columns, observations are by rows |
max.lag |
the maximal lag value |
method |
a feature selection algorithm. Implemented algorithms:
|
show.progress |
the logical parameter to print progress of calculation. By default is FALSE. |
localized |
the logical parameter to executed localized (component-wise) feature selection if the selected method supports this ("MI", "GLASSO", "RF"). Localized versions of algorithms are based on selection of features for independently for every MTS component from all lagged components. Non-localised versions include simulteneous feature selection for all components, including potential instantaneous effects (relationships between feature within the same lag). Leter, non-localised algortihms ignore instantaneous effects and return only lagged features. By default is TRUE |
... |
method-specific parameters:
|
The function implements selection of potential relationships between multivariate time series' components and their lags.
returns a real-valued or binary (depends on the algorithm) feature matrix of k*max.lag rows and k columns, where k is number of time series components (number of columns in the mts parameter). Columns correpond to components of the time series; rows correspond to lags (from 1 to max.lag).
Distance-based feature selection for MTS
Pfeifer, P. E., & Deutsch, S. J. 1980. A Three-Stage Iterative Procedure for Space-Time Modeling. Technometrics, 22(1), 35.
Cross-corelation-based feature selection for MTS
Netoff I., Caroll T.L., Pecora L.M., Sciff S.J. 2006. Detecting coupling in the presence of noise and nonlinearity. In: Schelter B, Winterhalder W, Timmer J, editors. Handbook of time series analysis.
Mutual information-based feature selection for MTS
Liu, T., Wei, H., Zhang, K., Guo, W., 2016. Mutual information based feature selection for multivariate time series forecasting, in: 35th Chinese Control Conference (CCC). Presented at the 2016 35th Chinese Control Conference (CCC), IEEE, Chengdu, China, pp. 7110–7114.
Random forest-based feature selection for MTS
Pavlyuk, D., 2020. Random Forest Variable Selection for Sparse Vector Autoregressive Models, in: Valenzuela, O., Rojas, F., Pomares, H., Rojas, I. (Eds.), Theory and Applications of Time Series Analysis. Selected Contributions from ITISE 2019., Contributions to Statistics.
Graphical LASSO-based feature selection for MTS
Haworth, J., Cheng, T., 2014. Graphical LASSO for local spatio-temporal neighbourhood selection, in: Proceedings the GIS Research UK 22nd Annual Conference. Presented at the GIS Research UK 22nd Annual Conference, Leicester, UK, pp. 425–433.
Least angle regression for feature selection for MTS
Gelper S. and Croux C., 2008. Least angle regression for time series forecasting with many predictors, Leuven, Belgium, p.37.
Partial spectral coherence for feature selection for MTS
Davis, R.A., Zang, P., Zheng, T., 2016. Sparse Vector Autoregressive Modeling. Journal of Computational and Graphical Statistics 25, 1077–1096.
# Load traffic data data(traffic.mini) # Scaling is sometimes useful for feature selection # Exclude the first column - it contains timestamps data <- scale(traffic.mini$data[,-1]) mIndep<-fsMTS(data, max.lag=3, method="ownlags") mCCF<-fsMTS(data, max.lag=3, method="CCF") mDistance<-fsMTS(data, max.lag=3, method="distance", shortest = traffic.mini$shortest, step = 5) mGLASSO<-fsMTS(data, max.lag=3,method="GLASSO", rho = 0.05) mLARS<-fsMTS(data, max.lag=3,method="LARS") mRF<-fsMTS(data, max.lag=3,method="RF") mMI<-fsMTS(data, max.lag=3,method="MI") mlist <- list(Independent = mIndep, Distance = mDistance, CCF = mCCF, GLASSO = mGLASSO, LARS = mLARS, RF = mRF, MI = mMI) th<-0.30 (msimilarity <- fsSimilarityMatrix(mlist,threshold = th, method="Kuncheva"))
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