Seasonal-trend decomposition for Rust
Add this line to your application’s Cargo.toml under [dependencies]:
toml
stlrs = "0.1"
Decompose a time series
```rust 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 = stlrs::params().fit(&series, period); ```
Get the components
rust
res.seasonal();
res.trend();
res.remainder();
Use robustness iterations
rust
let res = stlrs::params().robust(true).fit(&series, period);
Get robustness weights
rust
res.weights();
Set parameters
rust
stlrs::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
This library was ported from the Fortran implementation.
View the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
To get started with development:
sh
git clone https://github.com/ankane/stl-rust.git
cd stl-rust
cargo test