tspredit (version 1.2.747)

ts_mlp: MLP

Description

Create a time series prediction object based on a Multilayer Perceptron (MLP) regressor.

It wraps the nnet package to train a single-hidden-layer neural network on sliding-window inputs. Use ts_regsw utilities to project inputs/outputs.

Usage

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

Value

A ts_mlp object (S3) inheriting from ts_regsw.

Arguments

preprocess

Normalization preprocessor (e.g., ts_norm_gminmax()).

input_size

Integer. Number of lagged inputs used by the model.

size

Integer. Number of hidden neurons.

decay

Numeric. L2 weight decay (regularization) parameter.

maxit

Integer. Maximum number of training iterations.

Details

The MLP is a universal function approximator capable of learning non-linear mappings from lagged inputs to next-step values. For stability, consider normalizing inputs (e.g., ts_norm_gminmax()). Hidden size and weight decay control capacity and regularization respectively.

References

• D. E. Rumelhart, G. E. Hinton, and R. J. Williams (1986). Learning representations by back-propagating errors. Nature 323, 533–536.

• W. N. Venables and B. D. Ripley (2002). Modern Applied Statistics with S. Fourth Edition. Springer. (for the nnet package)

Examples

# Example: MLP on sliding windows with min–max normalization
# Load package and dataset
library(daltoolbox)
data(tsd)
ts <- ts_data(tsd$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)

# Prepare projection (X, y)
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

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

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

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