Package: echos 1.0.4

echos: Echo State Networks for Time Series Modeling and Forecasting

Provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).

Authors:Alexander Häußer [aut, cre, cph]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
echos/json (API)

# Install 'echos' in R:
install.packages('echos', repos = c('https://ahaeusser.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ahaeusser/echos/issues

Pkgdown/docs site:https://ahaeusser.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

echo-state-networksfablefabletoolsforecastforecastingrecurrent-neural-networksreservoir-computingridge-regressiontime-seriesopenblascppopenmp

6.97 score 19 stars 19 scripts 356 downloads 10 exports 43 dependencies

Last updated from:08e24fbb0b. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK213
linux-devel-x86_64OK234
source / vignettesOK227
linux-release-arm64OK245
linux-release-x86_64OK219
macos-release-arm64OK188
macos-release-x86_64OK326
macos-oldrel-arm64OK126
macos-oldrel-x86_64OK299
windows-develOK232
windows-releaseOK180
windows-oldrelOK257
wasm-releaseOK148

Exports:ESNfilter_esnforecast_esnis.esnis.forecast_esnis.tune_esnreservoirrun_reservoirtrain_esntune_esn

Dependencies:anytimeBHclicpp11digestdistributionaldplyrfabletoolsfarvergenericsggdistggplot2gluegtableisobandlabelinglifecyclelubridatemagrittrnumDerivpillarpkgconfigprogressrpurrrquadprogR6RColorBrewerRcppRcppArmadillorlangS7scalesstringistringrtibbletidyrtidyselecttimechangetsibbleutf8vctrsviridisLitewithr

Rolling forecasts
Introduction | Load packages | Prepare the data | Define the rolling forecast setup | Create rolling training windows | Train the ESN models | Generate rolling forecasts | Evaluate forecast accuracy | Visualize the rolling forecasts | Summary
Rendered fromvignette_202_tidy_m4_monthly_splits.Rmdusingknitr::rmarkdown

Last update: 2026-06-21
Started: 2026-06-21

Base functions
Load package | Prepare dataset | ESN architecture and automatic model selection | Train ESN model | Forecast ESN model | Hyperparameter tuning
Rendered fromvignette_100_base_airpassengers.Rmdusingknitr::rmarkdown

Last update: 2026-06-21
Started: 2026-06-21

Datasets
Introduction | Load packages | M4 data | Synthetic data
Rendered fromvignette_300_datasets.Rmdusingknitr::rmarkdown

Last update: 2026-06-21
Started: 2026-06-21

Multiple models
Load package | Prepare dataset | Train ESN model | Forecast ESN model
Rendered fromvignette_201_tidy_m4_monthly_models.Rmdusingknitr::rmarkdown

Last update: 2026-06-21
Started: 2026-06-21