ts_integtune: Time Series Integrated Tune

In tspredit: Time Series Prediction with Integrated Tuning

Time Series Integrated Tune

Description

Integrated tuning over input sizes, preprocessing, augmentation, and model hyperparameters for time series.

Usage

ts_integtune(
  input_size,
  base_model,
  folds = 10,
  ranges = NULL,
  preprocess = list(ts_norm_gminmax()),
  augment = list(ts_aug_none())
)

Arguments

input_size	Integer vector. Candidate input window sizes.
base_model	Base model object for tuning.
folds	Integer. Number of cross-validation folds.
ranges	Named list of hyperparameter ranges to explore.
preprocess	List of preprocessing objects to compare.
augment	List of augmentation objects to apply during training.

Value

A ts_integtune object.

References

Salles, R., Pacitti, E., Bezerra, E., Marques, C., Pacheco, C., Oliveira, C., Porto, F., Ogasawara, E. (2023). TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction Models. Lecture Notes in Computer Science.

Examples

# Integrated search over input size, preprocessing and model hyperparameters
library(daltoolbox)
data(tsd)

# Build windows and split into train/test, then project to (X, y)
ts <- ts_data(tsd$y, 10)
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Configure integrated tuning: ranges for input_size, ELM (nhid, actfun), and preprocessors
tune <- ts_integtune(
  input_size = 3:5,
  base_model = ts_elm(),
  ranges = list(nhid = 1:5, actfun = c('purelin')),
  preprocess = list(ts_norm_gminmax())
)

# Run search; augmentation (if provided) is applied during training internally
model <- fit(tune, x = io_train$input, y = io_train$output)

# Forecast and evaluate on the held-out window
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

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