the Scaler for the Pre-Trained Long Short Term Memory (LSTM) Network

This dataset contains the means and standard deviations of the 1,000,000 datasets for training the Long Short Term Memory (LSTM) Network, which can be used to determine the number of factors.

Format

A list containing two vectors, each of length 20:

means

A numeric vector representing the means of the 20 features extracted from the 1,000,000 datasets.

sds

A numeric vector representing the standard deviations of the 20 features extracted from the 1,000,000 datasets.

Examples

data(data.scaler.LSTM)
print(data.scaler.LSTM)
#> $means
#>  [1]  3.85473363  2.23900553  1.91115334  1.67192030  1.48584157  1.33651388
#>  [7]  1.20787935  1.10923691  1.02212731  0.95019767  2.22708983  0.70510766
#> [13]  0.44185121  0.26740026  0.13896533  0.04232092 -0.02760048 -0.08016894
#> [19] -0.11475688 -0.13651167
#> 
#> $sds
#>  [1] 2.0233955 0.8674056 0.7633381 0.6960061 0.6323624 0.5730228 0.5323546
#>  [8] 0.4871191 0.4614935 0.4439322 1.8275074 0.6592427 0.5443661 0.4537732
#> [15] 0.3737929 0.3026467 0.2393471 0.1868065 0.1439237 0.1110071
#> 

data.scaler <- load.scaler()
print(data.scaler)
#> $means
#>  [1]  3.85473363  2.23900553  1.91115334  1.67192030  1.48584157  1.33651388
#>  [7]  1.20787935  1.10923691  1.02212731  0.95019767  2.22708983  0.70510766
#> [13]  0.44185121  0.26740026  0.13896533  0.04232092 -0.02760048 -0.08016894
#> [19] -0.11475688 -0.13651167
#> 
#> $sds
#>  [1] 2.0233955 0.8674056 0.7633381 0.6960061 0.6323624 0.5730228 0.5323546
#>  [8] 0.4871191 0.4614935 0.4439322 1.8275074 0.6592427 0.5443661 0.4537732
#> [15] 0.3737929 0.3026467 0.2393471 0.1868065 0.1439237 0.1110071
#>