| Type: | Package |
| Title: | Time Series Forecasting with Neural Networks |
| Version: | 0.9.9 |
| Description: | Automatic time series modelling with neural networks. Allows fully automatic, semi-manual or fully manual specification of networks. For details of the specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>; and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Depends: | generics |
| Imports: | forecast, glmnet, neuralnet, plotrix, MASS, tsutils, uroot, methods |
| Suggests: | thief |
| URL: | https://kourentzes.com/forecasting/2019/01/16/tutorial-for-the-nnfor-r-package/ |
| BugReports: | https://github.com/trnnick/nnfor/issues |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2023-11-14 23:23:37 UTC; Nikos |
| Author: | Nikolaos Kourentzes [aut, cre] |
| Maintainer: | Nikolaos Kourentzes <nikolaos@kourentzes.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-11-15 00:40:02 UTC |
nnfor: Time Series Forecasting with Neural Networks
Description
Automatic time series modelling with neural networks. Allows fully automatic, semi-manual or fully manual specification of networks. For details of the specification methodology see: (i) Crone and Kourentzes (2010) doi:10.1016/j.neucom.2010.01.017; and (ii) Kourentzes et al. (2014) doi:10.1016/j.eswa.2013.12.011.
Author(s)
Maintainer: Nikolaos Kourentzes nikolaos@kourentzes.com
See Also
Useful links:
-
https://kourentzes.com/forecasting/2019/01/16/tutorial-for-the-nnfor-r-package/
Report bugs at https://github.com/trnnick/nnfor/issues
Extreme learning machines for time series forecasting
Description
This function fits ELM neural networks for time series forecasting.
Usage
elm(
y,
m = frequency(y),
hd = NULL,
type = c("lasso", "ridge", "step", "lm"),
reps = 20,
comb = c("median", "mean", "mode"),
lags = NULL,
keep = NULL,
difforder = NULL,
outplot = c(FALSE, TRUE),
sel.lag = c(TRUE, FALSE),
direct = c(FALSE, TRUE),
allow.det.season = c(TRUE, FALSE),
det.type = c("auto", "bin", "trg"),
xreg = NULL,
xreg.lags = NULL,
xreg.keep = NULL,
barebone = c(FALSE, TRUE),
model = NULL,
retrain = c(FALSE, TRUE)
)
Arguments
y |
Input time series. Can be ts or msts object. |
m |
Frequency of the time series. By default it is picked up from y. |
hd |
Number of hidden nodes. This can be a vector, where each number represents the number of hidden nodes of a different hidden layer. Use NULL to automatically specify. |
type |
Estimation type for output layer weights. Can be "lasso" (lasso with CV), "ridge" (ridge regression with CV), "step" (stepwise regression with AIC) or "lm" (linear regression). |
reps |
Number of networks to train, the result is the ensemble forecast. |
comb |
Combination operator for forecasts when reps > 1. Can be "median", "mode" (based on KDE estimation) and "mean". |
lags |
Lags of y to use as inputs. If none provided then 1:frequency(y) is used. Use 0 for no univariate lags. |
keep |
Logical vector to force lags to stay in the model if sel.lag == TRUE. If NULL then it keep = rep(FALSE,length(lags)). |
difforder |
Vector including the differencing lags. For example c(1,12) will apply first and seasonal (12) differences. For no differencing use 0. For automatic selection use NULL. |
outplot |
Provide plot of model fit. Can be TRUE or FALSE. |
sel.lag |
Automatically select lags. Can be TRUE or FALSE. |
direct |
Use direct input-output connections to model strictly linear effects. Can be TRUE or FALSE. |
allow.det.season |
Permit modelling seasonality with deterministic dummies. |
det.type |
Type of deterministic seasonality dummies to use. This can be "bin" for binary or "trg" for a sine-cosine pair. With "auto" if ony a single seasonality is used and periodicity is up to 12 then "bin" is used, otherwise "trg". |
xreg |
Exogenous regressors. Each column is a different regressor and the sample size must be at least as long as the target in-sample set, but can be longer. |
xreg.lags |
This is a list containing the lags for each exogenous variable. Each list is a numeric vector containing lags. If xreg has 3 columns then the xreg.lags list must contain three elements. If NULL then it is automatically specified. |
xreg.keep |
List of logical vectors to force lags of xreg to stay in the model if sel.lag == TRUE. If NULL then all exogenous lags can be removed. |
barebone |
Use an alternative elm implementation (written in R) that is faster when the number of inputs is very high. Typically not needed. |
model |
A previously trained mlp object. If this is provided then the same model is fitted to y, without re-estimating any model parameters. |
retrain |
If a previous model is provided, retrain the network or not. If the network is retrained the size of the hidden layer is reset. |
Value
Return object of class elm. If barebone == TRUE then the object inherits a second class "elm.fast".
The function plot produces a plot the network architecture.
elm contains:
-
net- ELM networks. If it is of class "elm.fast" then this is NULL. -
hd- Number of hidden nodes. If it is of class "elm.fast" this is a vector with a different number for each repetition. -
W.in- NULL unless it is of class "elm.fast". Contains the input weights. -
W- Output layer weights for each repetition. -
b- Contains the output node bias for each training repetition. -
W.dct- Contains the direct connection weights if argument direct == TRUE. Otherwise is NULL. -
lags- Input lags used. -
xreg.lags-xreglags used. -
difforder- Differencing used. -
sdummy- Use of deterministic seasonality. -
ff- Seasonal frequencies detected in data (taken from ts or msts object). -
ff.det- Seasonal frequencies coded using deterministic dummies. -
det.type- Type of determistic seasonality. -
y- Input time series. -
minmax- Scaling structure. -
xreg.minmax- Scaling structure for xreg variables. -
comb- Combination operator used. -
type- Estimation used for output layer weights. -
direct- Presence of direct input-output connections. -
fitted- Fitted values. -
MSE- In-sample Mean Squared Error.
Note
To use elm with Temporal Hierarchies (thief package) see elm.thief.
The elm function by default calls the neuralnet function. If barebone == TRUE then it uses an alternative implementation (TStools:::elm.fast) which is more appropriate when the number of inputs is several hundreds.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
References
For an introduction to neural networks see: Ord K., Fildes R., Kourentzes N. (2017) Principles of Business Forecasting 2e. Wessex Press Publishing Co., Chapter 10.
For combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.
For variable selection see: Crone S.F., Kourentzes N. (2010) Feature selection for time series prediction – A combined filter and wrapper approach for neural networks. Neurocomputing, 73(10), 1923-1936.
For ELMs see: Huang G.B., Zhou H., Ding X. (2006) Extreme learning machine: theory and applications. Neurocomputing, 70(1), 489-501.
See Also
Examples
## Not run:
fit <- elm(AirPassengers)
print(fit)
plot(fit)
frc <- forecast(fit,h=36)
plot(frc)
## End(Not run)
ELM (fast) neural network.
Description
Fit ELM (fast) neural network. This is an ELM implementation that does not rely on neuralnets package.
Usage
elm.fast(
y,
x,
hd = NULL,
type = c("lasso", "ridge", "step", "ls"),
reps = 20,
comb = c("median", "mean", "mode"),
direct = c(FALSE, TRUE),
linscale = c(TRUE, FALSE),
output = c("linear", "logistic"),
core = c("FALSE", "TRUE"),
ortho = c(FALSE, TRUE)
)
Arguments
y |
Target variable. |
x |
Explanatory variables. Each column is a variable. |
hd |
Starting number of hidden nodes (scalar). Use NULL to automatically specify. |
type |
Estimation type for output layer weights. Can be "lasso" (lasso with CV), "ridge" (ridge regression with CV), "step" (stepwise regression with AIC) or "lm" (linear regression). |
reps |
Number of networks to train. |
comb |
Combination operator for forecasts when reps > 1. Can be "median", "mode" (based on KDE estimation) and "mean". |
direct |
Use direct input-output connections to model strictly linear effects. Can be TRUE or FALSE. |
linscale |
Scale inputs linearly between -0.8 to 0.8. If output == "logistic" then scaling is between 0 and 1. |
output |
Type of output layer. It can be "linear" or "logistic". If "logistic" then type must be set to "lasso". |
core |
If TRUE skips calculation of final fitted values and MSE. Called internally by |
ortho |
If TRUE then the initial weights between the input and hidden layers are orthogonal (only when number of input variable <= sample size). |
Value
An object of class "elm.fast".
The function plot produces a plot the network fit.
An object of class "elm.fast" is a list containing the following elements:
-
hd- Number of hidden nodes. This is a vector with a different number for each training repetition. -
W.in- Input weights for each training repetition. -
W- Output layer weights for each repetition. -
b- Output node bias for each training repetition. -
W.dct- Direct connection weights argument if direct == TRUE for each training repetition. Otherwuse NULL. -
fitted.all- Fitted values for each training repetition. -
fitted- Ensemble fitted values. -
y- Target variable. -
type- Estimation used for output layer weights. -
comb- Combination operator used. -
direct- Presence of direct input-output connections. -
minmax- If scaling is used this contains the scaling information for the target variable. -
minmax.x- If scaling is used this contains the scaling information for the input variables. -
MSE- In-sample Mean Squared Error.
Note
This implementation of ELM is more appropriate when the number of inputs is several hundreds. For time series modelling use elm instead.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
References
For combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.
For ELMs see: Huang G.B., Zhou H., Ding X. (2006) Extreme learning machine: theory and applications. Neurocomputing, 70(1), 489-501.
See Also
elm.
Examples
## Not run:
p <- 2000
n <- 150
X <- matrix(rnorm(p*n),nrow=n)
b <- cbind(rnorm(p))
Y <- X %*% b
fit <- elm.fast(Y,X)
print(fit)
## End(Not run)
ELM network for THieF.
Description
Function for ELM forecasting with Temporal Hierarchies.
Usage
elm.thief(y, h = NULL, ...)
Arguments
y |
Input time series. Can be ts or msts object. |
h |
Forecast horizon. If NULL then h is set to match frequency of time series. |
... |
Additional arguments passed to |
Value
An object of classes "forecast.net" and "forecast".
The function plot produces a plot of the forecasts.
An object of class "forecast.net" is a list containing the following elements:
-
method- The name of the forecasting method as a character string. -
mean- Point forecasts as a time series. -
all.mean- An array h x reps of all ensemble members forecasts, where reps are the number of ensemble members. -
x- The original time series (eitherfitused to create the network. -
fitted- Fitted values. Any values not fitted for the initial period of the time series are imputted with NA. -
residuals- Residuals from the fitted network.
Note
This function is created to work with Temporal Hierarchied (thief package). For conventional ELM networks use elm.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
References
For forecasting with temporal hierarchies see: Athanasopoulos G., Hyndman R.J., Kourentzes N., Petropoulos F. (2017) Forecasting with Temporal Hierarchies. European Journal of Operational research, 262(1), 60-74.
For combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.
See Also
Examples
## Not run:
library(thief)
frc <- thief(AirPassengers,forecastfunction=elm.thief)
plot(frc)
## End(Not run)
Forecast using ELM neural network.
Description
Create forecasts using ELM neural networks.
Usage
## S3 method for class 'elm'
forecast(object, h = NULL, y = NULL, xreg = NULL, ...)
Arguments
object |
ELM network object, produced using |
h |
Forecast horizon. If NULL then h is set to match frequency of time series. |
y |
Optionally forecast using different data than what the network was trained on. Expected to create havoc and do really bad things! |
xreg |
Exogenous regressors. Each column is a different regressor and the sample size must be at least as long as the target in-sample set plus the forecast horizon, but can be longer. Set it to NULL if no xreg inputs are used. |
... |
Unused argument. |
Value
An object of classes "forecast.net" and "forecast".
The function plot produces a plot of the forecasts.
An object of class "forecast.net" is a list containing the following elements:
-
method- The name of the forecasting method as a character string. -
mean- Point forecasts as a time series. -
all.mean- An array h x reps of all ensemble members forecasts, where reps are the number of ensemble members. -
x- The original time series used to create the network. -
fitted- Fitted values. -
residuals- Residuals from the fitted network.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
See Also
Examples
## Not run:
fit <- elm(AirPassengers)
plot(fit)
frc <- forecast(fit,h=36)
plot(frc)
## End(Not run)
Forecast using MLP neural network.
Description
Create forecasts using MLP neural networks.
Usage
## S3 method for class 'mlp'
forecast(object, h = NULL, y = NULL, xreg = NULL, ...)
Arguments
object |
MLP network object, produced using |
h |
Forecast horizon. If NULL then h is set to match frequency of time series. |
y |
Optionally forecast using different data than what the network was trained on. Expected to create havoc and do really bad things! |
xreg |
Exogenous regressors. Each column is a different regressor and the sample size must be at least as long as the target in-sample set plus the forecast horizon, but can be longer. Set it to NULL if no xreg inputs are used. |
... |
Unused argument. |
Value
An object of classes "forecast.net" and "forecast".
The function plot produces a plot of the forecasts.
An object of class "forecast.net" is a list containing the following elements:
-
method- The name of the forecasting method as a character string. -
mean- Point forecasts as a time series. -
all.mean- An array h x reps of all ensemble members forecasts, where reps are the number of ensemble members. -
x- The original time series used to create the network. -
fitted- Fitted values. -
residuals- Residuals from the fitted network.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
See Also
Examples
## Not run:
fit <- mlp(AirPassengers)
plot(fit)
frc <- forecast(fit,h=36)
plot(frc)
## End(Not run)
Apply minmax linear scaling to a vector.
Description
Apply minmax linear scaling to a vector.
Usage
linscale(x, minmax = NULL, rev = c(FALSE, TRUE))
Arguments
x |
Input vector. |
minmax |
minmax must be a list with elements "mn", "mx", "mn.orig" and "mx.orig", where "mn" and "mx" refer to the target min and max, and the remaining two refer to the current vector min and max. By default mn=-1 and mx=1. mn.orig and mx.orig can be missing, unless the scaling is reversed. |
rev |
Reverse scaling back to original: TRUE or FALSE. |
Value
Outputs a list with elements:
-
x- Scaled vector. -
minmax- List with resulting mn, mx, mn.orig and mx.orig. Can be used as input to reverse scaling.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
Examples
y <- rnorm(20)*100
sc <- linscale(y)
x <- sc$x
print(c(min(y),max(y)))
print(c(min(x),max(x)))
sc.rev <- linscale(x,minmax=sc$minmax,rev=TRUE)
print(c(min(sc.rev$x),max(sc.rev$x)))
Multilayer Perceptron for time series forecasting
Description
This function fits MLP neural networks for time series forecasting.
Usage
mlp(
y,
m = frequency(y),
hd = NULL,
reps = 20,
comb = c("median", "mean", "mode"),
lags = NULL,
keep = NULL,
difforder = NULL,
outplot = c(FALSE, TRUE),
sel.lag = c(TRUE, FALSE),
allow.det.season = c(TRUE, FALSE),
det.type = c("auto", "bin", "trg"),
xreg = NULL,
xreg.lags = NULL,
xreg.keep = NULL,
hd.auto.type = c("set", "valid", "cv", "elm"),
hd.max = NULL,
model = NULL,
retrain = c(FALSE, TRUE),
...
)
Arguments
y |
Input time series. Can be ts or msts object. |
m |
Frequency of the time series. By default it is picked up from y. |
hd |
Number of hidden nodes. This can be a vector, where each number represents the number of hidden nodes of a different hidden layer. |
reps |
Number of networks to train, the result is the ensemble forecast. |
comb |
Combination operator for forecasts when reps > 1. Can be "median", "mode" (based on KDE estimation) and "mean". |
lags |
Lags of y to use as inputs. If none provided then 1:frequency(y) is used. Use 0 for no univariate lags. |
keep |
Logical vector to force lags to stay in the model if sel.lag == TRUE. If NULL then it keep = rep(FALSE,length(lags)). |
difforder |
Vector including the differencing lags. For example c(1,12) will apply first and seasonal (12) differences. For no differencing use 0. For automatic selection use NULL. |
outplot |
Provide plot of model fit. Can be TRUE or FALSE. |
sel.lag |
Automatically select lags. Can be TRUE or FALSE. |
allow.det.season |
Permit modelling seasonality with deterministic dummies. |
det.type |
Type of deterministic seasonality dummies to use. This can be "bin" for binary or "trg" for a sine-cosine pair. With "auto" if ony a single seasonality is used and periodicity is up to 12 then "bin" is used, otherwise "trg". |
xreg |
Exogenous regressors. Each column is a different regressor and the sample size must be at least as long as the target in-sample set, but can be longer. |
xreg.lags |
This is a list containing the lags for each exogenous variable. Each list is a numeric vector containing lags. If xreg has 3 columns then the xreg.lags list must contain three elements. If NULL then it is automatically specified. |
xreg.keep |
List of logical vectors to force lags of xreg to stay in the model if sel.lag == TRUE. If NULL then all exogenous lags can be removed. The syntax for multiple xreg is the same as for xreg.lags. |
hd.auto.type |
Used only if hd==NULL. "set" fixes hd=5. "valid" uses a 20% validation set (randomly) sampled to find the best number of hidden nodes. "cv" uses 5-fold cross-validation. "elm" uses ELM to estimate the number of hidden nodes (experimental). |
hd.max |
When hd.auto.type is set to either "valid" or "cv" then this argument can be used to set the maximum number of hidden nodes to evaluate, otherwise the maximum is set automatically. |
model |
A previously trained mlp object. If this is provided then the same model is fitted to y, without re-estimating any model parameters. |
retrain |
If a previous model is provided, retrain the network or not. |
... |
Additional inputs for neuralnet function. |
Value
Return object of class mlp.
The function plot produces a plot the network architecture.
mlp contains:
-
net- MLP networks. -
hd- Number of hidden nodes. -
lags- Input lags used. -
xreg.lags-xreglags used. -
difforder- Differencing used. -
sdummy- Use of deterministic seasonality. -
ff- Seasonal frequencies detected in data (taken from ts or msts object). -
ff.det- Seasonal frequencies coded using deterministic dummies. -
det.type- Type of determistic seasonality. -
y- Input time series. -
minmax- Scaling structure. -
xreg.minmax- Scaling structure for xreg variables. -
comb- Combination operator used. -
fitted- Fitted values. -
MSE- In-sample Mean Squared Error. -
MSEH- Ifhd.auto.typeis set to either "valid" or "cv" an array of the MSE error for each network size is provided. Otherwise this is NULL.
Note
To use mlp with Temporal Hierarchies (thief package) see mlp.thief.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
References
For an introduction to neural networks see: Ord K., Fildes R., Kourentzes N. (2017) Principles of Business Forecasting 2e. Wessex Press Publishing Co., Chapter 10.
For combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.
For variable selection see: Crone S.F., Kourentzes N. (2010) Feature selection for time series prediction – A combined filter and wrapper approach for neural networks. Neurocomputing, 73(10), 1923-1936.
See Also
Examples
## Not run:
fit <- mlp(AirPassengers)
print(fit)
plot(fit)
frc <- forecast(fit,h=36)
plot(frc)
## End(Not run)
MLP network for THieF.
Description
Function for MLP forecasting with Temporal Hierarchies.
Usage
mlp.thief(y, h = NULL, ...)
Arguments
y |
Input time series. Can be ts or msts object. |
h |
Forecast horizon. If NULL then h is set to match frequency of time series. |
... |
Additional arguments passed to |
Value
An object of classes "forecast.net" and "forecast".
The function plot produces a plot of the forecasts.
An object of class "forecast.net" is a list containing the following elements:
-
method- The name of the forecasting method as a character string. -
mean- Point forecasts as a time series. -
all.mean- An array h x reps of all ensemble members forecasts, where reps are the number of ensemble members. -
x- The original time series (eitherfitused to create the network. -
fitted- Fitted values. Any values not fitted for the initial period of the time series are imputted with NA. -
residuals- Residuals from the fitted network.
Note
This function is created to work with Temporal Hierarchied (thief package). For conventional MLP networks use mlp.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
References
For forecasting with temporal hierarchies see: Athanasopoulos G., Hyndman R.J., Kourentzes N., Petropoulos F. (2017) Forecasting with Temporal Hierarchies. European Journal of Operational research, 262(1), 60-74.
For combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.
See Also
Examples
## Not run:
library(thief)
frc <- thief(AirPassengers,forecastfunction=mlp.thief)
plot(frc)
## End(Not run)
nnfor:Time Series Forecasting with Neural Networks
Description
The nnfor package provides automatic time series modelling with neural networks. It facilitates fully automatic, semi-manual or fully manual specification of networks, using multilayer perceptrons (mlp) and extreme learning machines (elm).
Note
You can find a tutorial how to use the package here.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
References
For an introduction to neural networks see: Ord K., Fildes R., Kourentzes N. (2017) Principles of Business Forecasting 2e. Wessex Press Publishing Co., Chapter 10.
For ensemble combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.
For variable selection see: Crone S.F., Kourentzes N. (2010) Feature selection for time series prediction – A combined filter and wrapper approach for neural networks. Neurocomputing, 73(10), 1923-1936.
Plot ELM network.
Description
Produces a plot of the ELM network architecture.
Usage
## S3 method for class 'elm'
plot(x, r = 1, ...)
Arguments
x |
ELM network object, produced using |
r |
Ensemple member to plot. |
... |
Unused argument. |
Value
None. Function produces a plot.
Note
Neurons are coloured with "lightgrey". Seasonal dummies are coloured with "lightpink" and xreg with "lightblue".
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
See Also
Examples
## Not run:
fit <- elm(AirPassengers)
print(fit)
plot(fit)
frc <- forecast(fit,h=36)
plot(frc)
## End(Not run)
Plot MLP network.
Description
Produces a plot of the MLP network architecture.
Usage
## S3 method for class 'mlp'
plot(x, r = 1, ...)
Arguments
x |
MLP network object, produced using |
r |
Ensemple member to plot. |
... |
Unused argument. |
Value
None. Function produces a plot.
Note
Neurons are coloured with "lightgrey". Seasonal dummies are coloured with "lightpink" and xreg with "lightblue".
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
See Also
Examples
## Not run:
fit <- mlp(AirPassengers)
print(fit)
plot(fit)
frc <- forecast(fit,h=36)
plot(frc)
## End(Not run)
Predictions for ELM (fast) network.
Description
Calculate predictions for ELM (fast) network.
Usage
## S3 method for class 'elm.fast'
predict(object, newx, na.rm = c(FALSE, TRUE), ...)
Arguments
object |
ELM network object, produced using |
newx |
Explanatory variables. Each column is a variable. |
na.rm |
If TRUE remove columns and object produces an ensemble forecast, then remove any members that give NA in their forecasts. |
... |
Unused argument. |
Value
Returns a list with:
-
Y.hat- Ensemble prediction. -
Y.all- Predictions of each training repetition.
Author(s)
Nikolaos Kourentzes, nikolaos@kourentzes.com
See Also
Examples
## Not run:
p <- 2000
n <- 150
X <- matrix(rnorm(p*n),nrow=n)
b <- cbind(rnorm(p))
Y <- X %*% b
fit <- elm.fast(Y,X)
predict(fit,X)
## End(Not run)