pca.elm_train {ICompELM} | R Documentation |
Training of PCA based ELM model for time series forecasting
Description
An Extreme Learning Machine is trained by utilizing the concept of Principal Component Analysis.
Usage
pca.elm_train(
train_data,
lags,
comps = lags,
center = TRUE,
scale = TRUE,
bias = TRUE,
actfun = "sig"
)
Arguments
train_data |
A univariate time series data. |
lags |
Number of lags to be considered. |
comps |
Number of independent components to be considered. Corresponds
to number of hidden nodes. Defaults to maximum value, i.e., |
center |
Whether to compute PCA on mean-adjusted data. |
scale |
Whether to compute PCA on variance-adjusted data. |
bias |
Whether to include bias term while computing output weights.
Defaults to |
actfun |
Activation function for the hidden layer. Defaults to
|
Details
An Extreme Learning Machine (ELM) is trained wherein the weights connecting the input layer and hidden layer are obtained using Principal Component Analysis (PCA), instead of being chosen randomly. The number of hidden nodes is determined by the number of principal components.
Value
A list containing the trained ICA-ELM model with the following components.
inp_weights |
Weights connecting the input layer to hidden layer,
obtained from the unmixing matrix |
out_weights |
Weights connecting the hidden layer to output layer. |
fitted.values |
Fitted values of the model. |
residuals |
Residuals of the model. |
h.out |
A data frame containing the hidden layer outputs (activation function applied) with columns representing hidden nodes and rows representing observations. |
data |
The univariate |
lags |
Number of lags used during training. |
comps |
Number of independent components considered for training. It determines the number of hidden nodes. |
center |
Whether the input data was mean-adjusted during training. |
scale |
Whether the input data was variance-adjusted during training. |
bias |
Whether bias node was included during training. |
actfun |
Activation function for the hidden layer.
See |
Activation functions
The activation function for the hidden layer must be one of the following.
sig
Sigmoid function:
radbas
Radial basis function:
hardlim
Hard-limit function:
hardlims
Symmetric hard-limit function:
satlins
Symmetric saturating linear function:
tansig
Tan-sigmoid function:
tribas
Triangular basis function:
poslin
Postive linear function:
References
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science, 2(11), 559-572. doi:10.1080/14786440109462720.
Castaño, A., Fernández-Navarro, F., & Hervás-Martínez, C. (2013). PCA-ELM: a robust and pruned extreme learning machine approach based on principal component analysis. Neural processing letters, 37, 377-392. doi:10.1007/s11063-012-9253-x.
See Also
pca.elm_forecast()
for forecasing from trained PCA based ELM
model.
Examples
train_set <- head(price, 12*12)
pca.model <- pca.elm_train(train_data = train_set, lags = 12)