Adding `stlrs` library as a dependency

Run this command in a terminal, in your project's directory:

cargo add stlrs

To add it manually, edit your project's Cargo.toml file and add to the [dependencies] section:

stlrs = "0.3.1"

The stlrs library will be automatically available globally. Read the stlrs library documentation.

Back to the crate overview.

Readme

STL Rust

Seasonal-trend decomposition for Rust

🎉 Zero dependencies

Installation

Add this line to your application’s Cargo.toml under [dependencies]:

stlrs = "0.3"

Getting Started

Decompose a time series

use stlrs::Stl;

let series = vec![
    5.0, 9.0, 2.0, 9.0, 0.0, 6.0, 3.0, 8.0, 5.0, 8.0,
    7.0, 8.0, 8.0, 0.0, 2.0, 5.0, 0.0, 5.0, 6.0, 7.0,
    3.0, 6.0, 1.0, 4.0, 4.0, 4.0, 3.0, 7.0, 5.0, 8.0
];
let period = 7; // period of the seasonal component

let res = Stl::fit(&series, period).unwrap();

Get the components

res.seasonal();
res.trend();
res.remainder();

Robustness

Use robustness iterations

let res = Stl::params().robust(true).fit(&series, period).unwrap();

Get robustness weights

res.weights();

Multiple Seasonality

Specify multiple periods

use stlrs::Mstl;

let res = Mstl::fit(&series, &[7, 365]).unwrap();

Parameters

Set STL parameters

Stl::params()
    .seasonal_length(7)     // length of the seasonal smoother
    .trend_length(15)       // length of the trend smoother
    .low_pass_length(7)     // length of the low-pass filter
    .seasonal_degree(0)     // degree of locally-fitted polynomial in seasonal smoothing
    .trend_degree(1)        // degree of locally-fitted polynomial in trend smoothing
    .low_pass_degree(1)     // degree of locally-fitted polynomial in low-pass smoothing
    .seasonal_jump(1)       // skipping value for seasonal smoothing
    .trend_jump(2)          // skipping value for trend smoothing
    .low_pass_jump(1)       // skipping value for low-pass smoothing
    .inner_loops(2)         // number of loops for updating the seasonal and trend components
    .outer_loops(0)         // number of iterations of robust fitting
    .robust(false)          // if robustness iterations are to be used

Set MSTL parameters

Mstl::params()
    .iterations(2)                   // number of iterations
    .lambda(0.5)                     // lambda for Box-Cox transformation
    .seasonal_lengths(&[11, 15])     // lengths of the seasonal smoothers
    .stl_params(Stl::params())       // STL params

Strength

Get the seasonal strength

res.seasonal_strength();

Get the trend strength

res.trend_strength();

Credits

This library was ported from the Fortran implementation.

References

History

View the changelog

Contributing

Everyone is encouraged to help improve this project. Here are a few ways you can help:

Report bugs
Fix bugs and submit pull requests
Write, clarify, or fix documentation
Suggest or add new features

To get started with development:

git clone https://github.com/ankane/stl-rust.git
cd stl-rust
cargo test

Adding stlrs library as a dependency

Adding `stlrs` library as a dependency