fcast {GMDH}

A Function to Make Short Term Forecasting via GMDH-Type Neural Network Algorithms

Description

fcast forecasts time series via GMDH-type neural network algorithms.

Usage

fcast(data, method = "GMDH", input = 4, layer = 3, f.number = 5, level = 95,
tf = "all", weight = 0.7,lambda = c(0,0.01,0.02,0.04,0.08,0.16,0.32,0.64,
1.28,2.56,5.12,10.24))

Arguments

data

is a univariate time series of class ts

method

expects a character string to choose the desired method to forecast time series. To utilize GMDH-type neural network in forecasting, method is set to "GMDH". One should set method to "RGMDH" for forecasting via Revised GMDH-type neural network. Default is set to "GMDH"

input

is the number of inputs. Defaults input = 4

layer

is the number of layers. Default is set to layer = 3

f.number

is the number of observations to be forecasted. Defaults f.number = 5

level

confidence level for prediction interval. Default is set to 95

tf

expects a character string to choose the desired transfer function to be used in forecasting. To use polynomial function, tf should be set to "polynomial". Similarly, tf should be set to "sigmoid", "RBF", "tangent" to utilize sigmoid function, radial basis function and tangent function, respectively. To use all functions simultaneously, default is set to "all"

weight

is the percent of the data set to be utilized as learning set to estimate regularization parameter via validation. Default is set to weight = 0.70

lambda

is a vector which includes the sequence of feasible regularization parameters. Defaults lambda=c(0,0.01,0.02,0.04,0.08,0.16,0.32,0.64,1.28,2.56,5.12,10.24)

Value

Returns a list containing following elements:

method

the forecasting method as a character string

mean

point forecasts as a time series

lower

lower limits for prediction interval

upper

upper limits for prediction interval

level

confidence level for prediction interval

x

the original time series

fitted

the fitted values

residuals

the residuals of the model. The residuals are x minus the fitted values

Author(s)

Osman Dag, Ceylan Yozgatligil

References

Dag, O., Yozgatligil, C. (2016). GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms. The R Journal, 8:1, 379-386.

Ivakhnenko, A. G. (1966). Group Method of Data Handling - A Rival of the Method of Stochastic Approximation. Soviet Automatic Control, 13, 43-71.

Kondo, T., Ueno, J. (2006). Revised GMDH-Type Neural Network Algorithm With A Feedback Loop Identifying Sigmoid Function Neural Network. International Journal of Innovative Computing, Information and Control, 2:5, 985-996.

Examples

data = ts(rnorm(100, 10, 1))
out = fcast(data)
out

data = ts(rnorm(100, 10, 1))
out = fcast(data, input = 6, layer = 2, f.number = 1)
out$mean
out$fitted
out$residuals
plot(out$residuals)
hist(out$residuals)