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tsensembler
View on CRAN: Click
here
Download and install tsensembler package within the R console
Install from CRAN:
install.packages("tsensembler")
Install from Github:
library("remotes")
install_github("cran/tsensembler")
Install by package version:
library("remotes")
install_version("tsensembler", "0.1.0")
Attach the package and use:
library("tsensembler")
Maintained by
Vitor Cerqueira
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-08-28
Latest Update: 2020-10-27
Description:
A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.
How to cite:
Vitor Cerqueira (2017). tsensembler: Dynamic Ensembles for Time Series Forecasting. R package version 0.1.0, https://cran.r-project.org/web/packages/tsensembler
Previous versions and publish date:
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Functions, R codes and Examples using
the tsensembler R package
Some associated functions: ADE-class . ADE . DETS-class . DETS . EMASE . ade_hat-class . ade_hat . ae . base_ensemble-class . base_ensemble . base_models_loss . best_mvr . blocked_prequential . bm_cubist . bm_ffnn . bm_gaussianprocess . bm_gbm . bm_glm . bm_mars . bm_pls_pcr . bm_ppr . bm_randomforest . bm_svr . bm_xgb . build_base_ensemble . build_committee . combine_predictions . compute_predictions . dets_hat-class . dets_hat . embed_timeseries . get_target . get_top_models . get_y . holdout . intraining_estimations . intraining_predictions . l1apply . learning_base_models . loss_meta_learn . meta_cubist . meta_cubist_predict . meta_ffnn . meta_ffnn_predict . meta_gp . meta_gp_predict . meta_lasso . meta_lasso_predict . meta_mars . meta_mars_predict . meta_pls . meta_pls_predict . meta_ppr . meta_ppr_predict . meta_predict . meta_rf . meta_rf_predict . meta_svr . meta_svr_predict . meta_xgb . meta_xgb_predict . model_recent_performance . model_specs-class . model_specs . model_weighting . mse . normalize . predict-methods . predict_pls_pcr . proportion . rbind_l . recent_lambda_observations . rmse . roll_mean_matrix . se . select_best . sequential_reweighting . sliding_similarity . soft.completion . softmax . split_by . train_ade . train_ade_quick . tsensembler . update_ade . update_ade_meta . update_base_models . update_weights . water_consumption . xgb_optimizer . xgb_predict . xgb_predict_ .
Some associated R codes: data.R . ensembling-pipes.R . forecast.R . meta-modeling.R . sequential-reweight.R . ts-preprocess.R . utils.R . Full tsensembler package functions and examples
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