# echos 1.0.4

## Improvements
* Expanded package documentation, vignettes and README to better describe the ESN architecture, key hyperparameters, model selection procedure and tuning workflow.
* Clarified the role of the `formula` argument in `ESN()` for the `fabletools` model interface.

## Datasets
* Renamed `m4_data` to `m4_monthly_subset` to clarify that the dataset contains a monthly subset of the M4 competition data. The old name `m4_data` is retained for backward compatibility.
* Improved documentation for `m4_monthly_subset` and `synthetic_data`.

# echos 1.0.3

## New features
* Added reservoir scaling parameter `tau` to `train_esn()`, enabling dynamic control of reservoir size.
* Added `tune_esn()` to tune hyperparameters `alpha`, `rho` and `tau` via time series cross-validation (i.e., rolling forecasts).
* Added S3 methods `summary.tune_esn()` and `plot.tune_esn()` to summarize and visualize results from hyperparameter tuning.

## Bug fixes
* Fixed `train_esn()` so `n_initial` is only auto-set when `NULL`.

## Improvements
* Added input validation for `y` and `inf_crit` in `train_esn()` and `levels` in `forecast_esn()`.
* Improved documentation

# echos 1.0.2

## New features
* Added forecast intervals to `forecast_esn()`, `forecast.ESN()` and `plot.forecast_esn()`. Forecast intervals are generated by simulating future sample path based on a moving block bootstrap of the residuals and estimating the quantiles from the simulations.
* Added `plot.esn()` to visualize the internal states (i.e., the reservoir).
* Added `filter_esn()` to extract ESN models from a mable.
* Added `synthetic_data`, a dataset with synthetic time series data as tibble.

## Improvements
* Improved documentation
* Added unit tests
* Reduced dependencies

# echos 1.0.1
* Updates due to CRAN comments

# echos 1.0.0
* Initial CRAN submission
