Crate augurs_ets

Exponential smoothing models.

This crate provides exponential smoothing models for time series forecasting. The models are implemented in Rust and are based on the statsforecast Python package.

Important: This crate is still in development and the API is subject to change. Seasonal models are not yet implemented, and some model types have not been tested.

Example

use augurs_ets::AutoETS;

let data: Vec<_> = (0..10).map(|x| x as f64).collect();
let mut search = AutoETS::new(1, "ZZN")
    .expect("ZZN is a valid model search specification string");
let model = search.fit(&data).expect("fit should succeed");
let forecast = model.predict(5, 0.95);
assert_eq!(forecast.point.len(), 5);
assert_eq!(forecast.point, vec![10.0, 11.0, 12.0, 13.0, 14.0]);

Modules

• data
  Data for testing and examples.
• model
  A single model of the ETS family.

Structs

• AutoETS
  Automatic ETS model selection.
• AutoSpec
  Auto model search specification.

Enums

• Error
  Errors returned by this crate.