ts_maintune: Time Series Tune

In tspredit: Time Series Prediction with Integrated Tuning

Time Series Tune

Description

Time Series Tune

Usage

ts_maintune(
  input_size,
  base_model,
  folds = 10,
  preprocess = list(ts_norm_gminmax()),
  augment = list(ts_aug_none())
)

Arguments

input_size	input size for machine learning model
base_model	base model for tuning
folds	number of folds for cross-validation
preprocess	list of preprocessing methods
augment	data augmentation method

Value

a ts_maintune object.

Examples

library(daltoolbox)
data(tsd)
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)

tune <- ts_maintune(input_size=c(3:5), base_model = ts_elm(), preprocess = list(ts_norm_gminmax()))
ranges <- list(nhid = 1:5, actfun=c('purelin'))

# Generic model tunning
model <- fit(tune, x=io_train$input, y=io_train$output, ranges)

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 June 22, 2025, 5:07 p.m.