ts_tune: Time Series Tune

In tspredit: Time Series Prediction with Integrated Tuning

Time Series Tune

Description

Create a ts_tune object for hyperparameter tuning of a time series model.

Sets up a cross-validated search over hyperparameter ranges and input sizes for a base model. Results include the evaluated configurations and the selected best configuration.

Usage

ts_tune(input_size, base_model, folds = 10, ranges = NULL)

Arguments

input_size	Integer vector. Candidate input window sizes.
base_model	Base model object to tune (e.g., ts_mlp()).
folds	Integer. Number of cross-validation folds.
ranges	Named list of hyperparameter ranges to explore.

Value

A ts_tune object.

References

• R. Kohavi (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI.

• 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

# Example: grid search over input_size and ELM hyperparameters
# Load library and example data
library(daltoolbox)
data(tsd)

# Prepare 10-lag windows and split into train/test
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)

# Define tuning: vary input_size and ELM hyperparameters (nhid, actfun)
tune <- ts_tune(
  input_size = 3:5,
  base_model = ts_elm(ts_norm_gminmax()),
  ranges = list(nhid = 1:5, actfun = c('purelin'))
)

# Run CV-based search and get the best fitted model
model <- fit(tune, x = io_train$input, y = io_train$output)

# Forecast and evaluate on the held-out horizon
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.