3 unstable releases
0.3.1 | Jul 30, 2024 |
---|---|
0.3.0 | Jul 30, 2024 |
0.2.0 | Jun 5, 2024 |
#7 in #forecasting
59 downloads per month
45KB
977 lines
High level forecasting API for the augurs time series library
augurs-forecaster
contains a high-level API for training and predicting with time series models. It currently allows you to combine a model with a set of transformations (such as imputation of missing data, min-max scaling, and log/logit transforms) and fit the model on the transformed data, automatically handling back-transformation of forecasts and prediction intervals.
Usage
First add this crate, augurs-core
, and any required model crates to your Cargo.toml
:
[dependencies]
augurs-ets = { version = "*", features = ["mstl"] }
augurs-forecaster = "*"
augurs-mstl = "*"
use augurs_ets::{AutoETS, trend::AutoETSTrendModel};
use augurs_forecaster::{Forecaster, Transform, transforms::MinMaxScaleParams};
use augurs_mstl::MSTLModel;
let data = &[
1.0, 1.2, 1.4, 1.5, f64::NAN, 1.4, 1.2, 1.5, 1.6, 2.0, 1.9, 1.8
];
// Set up the model. We're going to use an MSTL model to handle
// multiple seasonalities, with a non-seasonal `AutoETS` model
// for the trend component.
// We could also use any model that implements `augurs_core::Fit`.
let ets = AutoETS::non_seasonal().into_trend_model();
let mstl = MSTLModel::new(vec![2], ets);
// Set up the transforms.
let transforms = vec![
Transform::linear_interpolator(),
Transform::min_max_scaler(MinMaxScaleParams::from_data(data.iter().copied())),
Transform::log(),
];
// Create a forecaster using the transforms.
let mut forecaster = Forecaster::new(mstl).with_transforms(transforms);
// Fit the forecaster. This will transform the training data by
// running the transforms in order, then fit the MSTL model.
forecaster.fit(&data).expect("model should fit");
// Generate some in-sample predictions with 95% prediction intervals.
// The forecaster will handle back-transforming them onto our original scale.
let in_sample = forecaster
.predict_in_sample(0.95)
.expect("in-sample predictions should work");
// Similarly for out-of-sample predictions:
let out_of_sample = forecaster
.predict(5, 0.95)
.expect("out-of-sample predictions should work");
Dependencies
~0.7–1.2MB
~25K SLoC