Struct SlidingWindowFeatures 

pub struct SlidingWindowFeatures { /* private fields */ }

Sliding Window Feature Extractor

Extracts features from sliding windows of time series data including various statistical measures, moving averages, and volatility metrics.

Parameters

• window_size - Size of the sliding window
• step_size - Step size for window sliding
• features - List of features to extract (“mean”, “std”, “min”, “max”, “median”, “volatility”, “skewness”, “kurtosis”)

Examples

use sklears_feature_extraction::temporal_features::SlidingWindowFeatures;
use scirs2_core::ndarray::{array, Array1, ArrayView1, ArrayView2};

let ts_data = array![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
let extractor = SlidingWindowFeatures::new()
    .window_size(5)
    .step_size(1)
    .features(vec!["mean".to_string(), "std".to_string(), "volatility".to_string()]);

let features = extractor.extract_features(&ts_data.view()).expect("valid time series produces features");

Implementations

impl SlidingWindowFeatures

pub fn new() -> Self

Create a new SlidingWindowFeatures extractor

pub fn window_size(self, window_size: usize) -> Self

Set the window size

pub fn step_size(self, step_size: usize) -> Self

Set the step size

pub fn features(self, features: Vec<String>) -> Self

Set the features to extract

pub fn extract_features( &self, ts: &ArrayView1<'_, Float>, ) -> SklResult<Array2<Float>>

Extract sliding window features from a time series

Trait Implementations

impl Clone for SlidingWindowFeatures

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SlidingWindowFeatures

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for SlidingWindowFeatures

fn default() -> Self

Returns the “default value” for a type.