ts_mlp: MLP

In tspredit: Time Series Prediction with Integrated Tuning

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)

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.

Value

A ts_mlp object (S3) inheriting from ts_regsw.

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

tspredit documentation built on Feb. 11, 2026, 9:08 a.m.