Provides four different classification machine learning methods and one C5.0 rule model to predict the best performing forecasting method for time series. The four different classification methods are: xgboost, cat boost, svm and ann. This package contains labeled time series data to train the models for the prediction of new time series data.
# Initialize the R package
library(tsfcmethodr)
# Example of a xgboost classification model with the basic ts
# taxonomy from tstaxonomyr R package --------
# Train a xgboost model
fitted_model <- train_xgb(n_round = 10, cv_nfold = 10,
tune_length = 10, ts_taxonomy = "v1")
# Predict best performing forecasting method for a new classified ts
ts_sales = datasets::BJsales
prediction <- predict_fc_model(fitted_model, ts_sales, "v1")
prediction
# Example of a svm classification model with the ligther feature
# selected ts taxonomy from tstaxonomyr R package --------
# Train a svm model
fitted_model <- train_svm(n_round = 10, cv_nfold = 10,
tune_length = 10, ts_taxonomy = "v2")
# Predict best performing forecasting method for a new classified ts
ts_sales = datasets::BJsales
prediction <- predict_fc_model(fitted_model, ts_sales, "v2")
prediction
You can install the development version 1.0.0 from Github with:
devtools::install_github("mowomoyela/tsfcmethdr")
All provided functions of this package:
This package is free and open source software, licensed under GPL-2.
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