ts_mlp: MLP

In daltoolbox: Leveraging Experiment Lines to Data Analytics

MLP

Description

Creates a time series prediction object that uses the Multilayer Perceptron (MLP). It wraps the nnet library.

Usage

ts_mlp(preprocess = NA, input_size = NA, size = NA, decay = 0.01, maxit = 1000)

Arguments

preprocess	normalization
input_size	input size for machine learning model
size	number of neurons inside hidden layer
decay	decay parameter for MLP
maxit	maximum number of iterations

Value

a ts_mlp object.

Examples

data(sin_data)
ts <- ts_data(sin_data$y, 10)
ts_head(ts, 3)

samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

model <- ts_mlp(ts_norm_gminmax(), input_size=4, size=4, decay=0)
model <- fit(model, x=io_train$input, y=io_train$output)

prediction <- predict(model, x=io_test$input[1,], steps_ahead=5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

ev_test <- evaluate(model, output, prediction)
ev_test

daltoolbox documentation built on May 29, 2024, 1:57 a.m.