knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", cache = TRUE, message = FALSE, warning = FALSE )
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.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.