The package provides a tidy interface for modeling and forecasting univariate time series using Echo State Networks (ESNs). The model works within the fable framework provided by the fabletools package, which provides the tools to evaluate, visualize, and combine models in a workflow consistent with the tidyverse.
Disclaimer: The echos package is highly experimental and it is very likely that there will be (substantial) changes in the near future. These changes will probably affect the interface (e.g. arguments within ESN()) and the underlying modeling procedure itself.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("ahaeusser/echos")Getting started
library(echos)
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
# Forecast horizon
n_ahead <- 12 # forecast horizon
# Number of observations
n_obs <- length(AirPassengers)
# Number of observations for training
n_train <- n_obs - n_ahead
# Prepare train and test data
xtrain <- AirPassengers[(1:n_train)]
xtest <- AirPassengers[((n_train+1):n_obs)]
# Train and forecast ESN model
xmodel <- train_esn(y = xtrain)
xfcst <- forecast_esn(xmodel, n_ahead = n_ahead)
# Plot result
plot(xfcst, test = xtest)
