timelag — creating time-lagged time series data

This crate provides the lag_matrix and related functions to create time-lagged versions of time series similar to MATLAB's lagmatrix for time series analysis.

Support for ndarray's Array1 and Array2 traits is available via the ndarray crate feature.

Examples

For singular time series:

use timelag::lag_matrix;

fn singular_series() {
    let data = [1.0, 2.0, 3.0, 4.0];
    
    // Using infinity for padding because NaN doesn't equal itself.
    let lag = f64::INFINITY;
    let padding = f64::INFINITY;
    
    // Create three lagged versions.
    // Use a stride of 5 for the rows, i.e. pad with one extra entry.
    let lagged = lag_matrix(&data, 0..=3, lag, 5).unwrap();

    // The function is also available via the CreateLagMatrix.
    // All methods take an IntoIterator<Item = usize> for the lags.
    let other = data.lag_matrix([0, 1, 2, 3], lag, 5).unwrap();
    
    assert_eq!(
        lagged,
        &[
            1.0, 2.0, 3.0, 4.0, padding, // original data
            lag, 1.0, 2.0, 3.0, padding, // first lag
            lag, lag, 1.0, 2.0, padding, // second lag
            lag, lag, lag, 1.0, padding, // third lag
        ]
    );
    assert_eq!(lagged, other);
}

For matrices with time series along their rows:

use timelag::{lag_matrix_2d, MatrixLayout};

fn matrix_rows() {
    let data = [
         1.0,  2.0,  3.0,  4.0,
        -1.0, -2.0, -3.0, -4.0
    ];

    // Using infinity for padding because NaN doesn't equal itself.
    let lag = f64::INFINITY;
    let padding = f64::INFINITY;

    let lagged = lag_matrix_2d(&data, MatrixLayout::RowWise(4), 3, lag, 5).unwrap();

    assert_eq!(
        lagged,
        &[
             1.0,  2.0,  3.0,  4.0, padding, // original data
            -1.0, -2.0, -3.0, -4.0, padding,
             lag,  1.0,  2.0,  3.0, padding, // first lag
             lag, -1.0, -2.0, -3.0, padding,
             lag,  lag,  1.0,  2.0, padding, // second lag
             lag,  lag, -1.0, -2.0, padding,
             lag,  lag,  lag,  1.0, padding, // third lag
             lag,  lag,  lag, -1.0, padding,
        ]
    );
}

For matrices with time series along their columns:

use timelag::{lag_matrix_2d, MatrixLayout};

fn matrix_columns() {
    let data = [
        1.0, -1.0,
        2.0, -2.0,
        3.0, -3.0,
        4.0, -4.0
    ];

    // Using infinity for padding because NaN doesn't equal itself.
    let lag = f64::INFINITY;
    let padding = f64::INFINITY;

    // Example row stride of nine: 2 time series × (1 original + 3 lags) + 1 extra padding.
    let lagged = lag_matrix_2d(&data, MatrixLayout::ColumnWise(4), 3, lag, 9).unwrap();

    assert_eq!(
        lagged,
        &[
        //   original
        //   |-----|    first lag
        //   |     |     |-----|    second lag
        //   |     |     |     |     |-----|    third lag
        //   |     |     |     |     |     |     |-----|
        //   ↓     ↓     ↓     ↓     ↓     ↓     ↓     ↓
            1.0, -1.0,  lag,  lag,  lag,  lag,  lag,  lag, padding,
            2.0, -2.0,  1.0, -1.0,  lag,  lag,  lag,  lag, padding,
            3.0, -3.0,  2.0, -2.0,  1.0, -1.0,  lag,  lag, padding,
            4.0, -4.0,  3.0, -3.0,  2.0, -2.0,  1.0, -1.0, padding
        ]
    );
}