Dynamic Ensembles for Time Series Forecasting
Overview
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.
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Vitor Cerqueira <cerqueira.vitormanuel@gmail.com>’ Package CITATION file contains call(s) to old-style personList() or as.personList(). Please use c() on person objects instead. Package CITATION file contains call(s) to old-style citEntry(). Please use bibentry() instead.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 92 (73 internal)includes a package overview page
- Examples that run
- 83%15 run in a plain check · 3 never run
- Exported functions with examples
- 16%
- Documented parameters
- 100%
- Return-value docs
- 18%
- Help pages citing references
- 5%
Downloads
Repository
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Checks run against github.com/vcerqueira/tsensembler on 2026-10-22.
No development-tooling practices detected in the upstream repository.
Dependencies
Installing tsensembler adds 38 packages (24 with compiled code, 47 MB of source tarballs). Heaviest: stringi 11 MB, data.table 5.7 MB, pls 4.2 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- unarchivedReturned to CRAN2020-10-27
- 0.1.0Latest2020-10-27 · current release · diff ↗ ·
removed 25 exports
FIFO, arima_predict, bm_arima, bm_ets, bm_tbats, bm_timeseries, build_committee_set, combine_committees, constructive_aggregation, constructive_aggregation_, contiguous_count, erfc, ets_predict, forecast, hat_info, merging_in_experts, prune_c_contiguity, prune_c_outperformance, rm.null, tbats_predict, unlistn, vcapply, viapply, vlapply, vnapplyadded 16 exports
meta_ffnn, meta_ffnn_predict, meta_mars, meta_mars_predict, meta_pls, meta_pls_predict, meta_ppr, meta_ppr_predict, meta_svr, meta_svr_predict, meta_xgb, meta_xgb_predict, quickADE, train_ade_quick, xgb_optimizer, xgb_predict_ - RR 4.0.0 released · 2020-04-24
- archivedRemoved from CRAN2020-01-16check issues were not corrected in time 'Additional issues'
- unarchivedReturned to CRAN2019-07-05
- 0.0.52019-07-05 · diff ↗ ·
removed 3 exports
mae, mase, r_squaredadded 11 exports
arima_predict, bm_arima, bm_ets, bm_tbats, bm_timeseries, bm_xgb, ets_predict, meta_cubist, meta_cubist_predict, tbats_predict, xgb_predict - archivedRemoved from CRAN2019-05-19check problems were not corrected in time
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 0.0.42018-04-13 · diff ↗ ·
removed 4 exports
arima_offline_forecast, arima_online_forecast, bm_auto_arima, predict_pls_pcradded 10 exports
build_committee_set, combine_committees, constructive_aggregation, constructive_aggregation_, contiguous_count, hat_info, holdout, merging_in_experts, prune_c_contiguity, prune_c_outperformance
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- 0.0.32018-03-10 · diff ↗ ·
added 15 exports
arima_offline_forecast, arima_online_forecast, bm_auto_arima, loss_meta_learn, meta_gp, meta_gp_predict, meta_lasso, meta_lasso_predict, meta_predict, meta_rf, meta_rf_predict, predict_pls_pcr, sequential_reweighting, sliding_similarity, soft.completion - 0.0.22017-08-28
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-08-28
- Total releases
- 5 / 9 yrs
- License
- GPL (>= 2) OSI
- Bundled data
- 13 KB / 1 file
- Download size
- 71 KB
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