tspredit (version 1.2.747)

ts_elm: ELM

Description

Create a time series prediction object that uses Extreme Learning Machine (ELM) regression.

It wraps the elmNNRcpp package to train single-hidden-layer networks with randomly initialized hidden weights and closed-form output weights.

Usage

ts_elm(preprocess = NA, input_size = NA, nhid = NA, actfun = "purelin")

Value

A ts_elm 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.

nhid

Integer. Hidden layer size.

actfun

Character. One of 'sig', 'radbas', 'tribas', 'relu', 'purelin'.

Details

ELMs are efficient to train and can perform well with appropriate hidden size and activation choice. Consider normalizing inputs and tuning nhid and the activation function.

References

• G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew (2006). Extreme Learning Machine: Theory and Applications. Neurocomputing, 70(1–3), 489–501.

Examples

# Example: ELM with sliding-window inputs
# Load package and toy dataset
library(daltoolbox)
data(tsd)

# Create sliding windows of length 10 (t9 ... t0)
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)

# Split last 5 rows as test set
samp <- ts_sample(ts, test_size = 5)
# Project to inputs (X) and outputs (y)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Define ELM with global min-max normalization and fit
model <- ts_elm(ts_norm_gminmax(), input_size = 4, nhid = 3, actfun = "purelin")
model <- fit(model, x = io_train$input, y = io_train$output)

# Forecast 5 steps ahead starting from the last known window
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

# Evaluate forecast error on the test horizon
ev_test <- evaluate(model, output, prediction)
ev_test