robustMultExpSmoothing: Robust Multivariate Exponential Smoothing
In arorar/covmat: Covariance Matrix Estimation

Description Usage Arguments Details Author(s)

Robust Multivariate Exponential Smoothing

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robustMultExpSmoothing(R, smoothMat = NA, startup_period = 10,
  training_period = 60, seed = 9999, trials = 50, method = "L-BFGS-B",
  lambda = 0.2)

`R`	data
`smoothMat`	Optimal smoothing matrix. If missing it is estimated. The procedure maybe very slow for high-dimensional data. Also, the objective function being very noisy, optimization across multiple runs may lead to different smoothing matrices. #'
`startup_period`	length of samples required to calculate initial values
`training_period`	length of samples required to calculate forecast errors for evalualating the objective if smoothing matrix is estimated
`seed`	random seed to replicate the starting values for optimization
`trials`	number of strarting values to try for any optimization. Large number of trials for high dimensions can be time consuming
`method`	optimization method to use to evaluate an estimate of smoothing matrix. Default is L-BFGS-B
`lambda`	known constant as described in the paper. Defaults to 0.2

Calculate Robust estimate of covariance matrix while also smoothing and cleaning the data using the procedure described in (Croux, Gelper, and Mahieu, 2010)

Rohit Arora

arorar/covmat documentation built on May 10, 2019, 1:48 p.m.