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echos

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The echos package provides a comprehensive set of functions and methods for modeling and forecasting univariate time series using Echo State Networks (ESNs). It offers two alternative approaches:

The package features a lightweight implementation that enables fast and fully automatic model training and forecasting using ESNs. You can quickly and easily build accurate ESN models without requiring extensive hyperparameter tuning or manual configuration.

Installation

You can install the stable version from CRAN:

install.packages("echos")

You can install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("ahaeusser/echos")

Base R

library(echos)

# 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)

Plot forecast and test data

Tidy R

library(echos)
library(tidyverse)
library(tsibble)
library(fable)

# Prepare train data
train_frame <- m4_data %>%
  filter(series %in% c("M21655", "M2717"))

# Train and forecast ESN model
train_frame %>%
  model(
    "ESN" = ESN(value),
    "ARIMA" = ARIMA(value)
    ) %>%
  forecast(h = 18) %>%
  autoplot(train_frame, level = NULL)

Plot forecast and train data

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.