ts_tune: Time Series Tune

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Time Series Tune

Description

Creates a ts_tune object for tuning hyperparameters of a time series model. This function sets up a tuning process for the specified base model by exploring different configurations of hyperparameters using cross-validation.

Usage

ts_tune(input_size, base_model, folds = 10)

Arguments

input_size	input size for machine learning model
base_model	base model for tuning
folds	number of folds for cross-validation

Value

returns a ts_tune object

Examples

data(sin_data)
ts <- ts_data(sin_data$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)

tune <- ts_tune(input_size=c(3:5), base_model = ts_elm(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

daltoolbox documentation built on Nov. 3, 2024, 9:06 a.m.