# Robust Multivariate Exponential Smoothing

### Description

Robust Multivariate Exponential Smoothing

### Usage

1 2 3 | ```
robustMultExpSmoothing(R, smoothMat = NA, startup_period = 10,
training_period = 60, seed = 9999, trials = 50, method = "L-BFGS-B",
lambda = 0.2)
``` |

### Arguments

`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 |

### Details

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

### Author(s)

Rohit Arora