Other packages > Find by keyword >

tsensembler  

Dynamic Ensembles for Time Series Forecasting
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]
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. Accessed 21 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:15), 0.0.2 (2017-08-28 14:20), 0.0.3 (2018-03-10 19:30), 0.0.4 (2018-04-13 22:11), 0.0.5 (2019-07-05 20:00)
Other packages that cited tsensembler R package
View tsensembler citation profile
Other R packages that tsensembler depends, imports, suggests or enhances
Complete documentation for tsensembler
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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

netgen  
Network Generator for Combinatorial Graph Problems
Methods for the generation of a wide range of network geographies, e.g., grid networks or clustered ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
truncnormbayes  
Estimates Moments for a Truncated Normal Distribution using 'Stan'
Finds the posterior modes for the mean and standard deviation for a truncated normal distribution wi ...
Download / Learn more Package Citations See dependency  
beastier  
Call 'BEAST2'
'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/ ...
Download / Learn more Package Citations See dependency  
splash  
Simple Process-Led Algorithms for Simulating Habitats
This program calculates bioclimatic indices and fluxes (radiation, evapotranspiration, soil moistur ...
Download / Learn more Package Citations See dependency  
OSTE  
Optimal Survival Trees Ensemble
Function for growing survival trees ensemble ('Naz Gul', 'Nosheen Faiz', 'Dan Brawn', 'Rafal Kulakow ...
Download / Learn more Package Citations See dependency  

27,889

R Packages

239,283

Dependencies

74,019

Author Associations

27,890

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA