NlinTS (version 1.4.5)

varmlp: Artificial Neural Network VAR (Vector Auto-Regressive) model using a MultiLayer Perceptron, with the sigmoid activation function. The optimization algorithm is based on the stochastic gradient descent.

Description

Artificial Neural Network VAR (Vector Auto-Regressive) model using a MultiLayer Perceptron, with the sigmoid activation function. The optimization algorithm is based on the stochastic gradient descent.

Usage

varmlp(
  df,
  lag,
  sizeOfHLayers,
  iters = 50,
  learningRate = 0.01,
  algo = "sgd",
  batch_size = 10,
  bias = TRUE,
  seed = 5,
  activations = vector()
)

Arguments

df

A numerical dataframe

lag

The lag parameter.

sizeOfHLayers

Integer vector that contains the size of hidden layers, where the length of this vector is the number of hidden layers, and the i-th element is the number of neurons in the i-th hidden layer.

iters

The number of iterations.

learningRate

The learning rate to use, O.1 by default, and if Adam algorithm is used, then it is the initial learning rate.

algo

String argument, for the optimisation algorithm to use, in choice ["sgd", "adam"]. By default "sgd" (stochastic gradient descent) is used. The algorithm 'adam' is to adapt the learning rate while using "sgd".

batch_size

Integer argument for the batch size used in the back-propagation algorithm.

bias

Logical, true if the bias have to be used in the network.

seed

Integer value for the seed used in the random generation of the weights of the network (a value = 0 will use the clock as random generator seed).

activations

String vector for the activations functions to use (in choice ["sigmoid", "relu", "tanh"]). The length of this vector is the number of hidden layers plus one (the output layer). By default, the relu activation function is used in hidden layers, and the sigmoid in the last layer.

Value

fit (df, iterations, batch_size): fit/update the weights of the model from the dataframe.

forecast (df): makes forecasts of an given dataframe. The forecasts include the forecasted row based on each previous "lag" rows, where the last one is the next forecasted row of df.

save (filename): save the model in a text file.

load (filename): load the model from a text file.

Details

This function builds the model, and returns an object that can be used to make forecasts and can be updated from new data.

Examples

# NOT RUN {
library (timeSeries) # to extract time series
library (NlinTS)
#load data
data = LPP2005REC
# Predict the last row of the data
train_data = data[1:(nrow (data) - 1), ]
model = varmlp (train_data, 1, c(10), 50, 0.01, "sgd", 30, TRUE, 0);
predictions = model$forecast (train_data)
print (predictions[nrow (predictions),])
# }