tspredit (version 1.2.747)

ts_integtune: 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())
)

Value

A ts_integtune object.

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.

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