/// @module std::core::utils::rolling
/// Rolling Window Operations
/// Optimized implementations using dedicated intrinsics for performance
/// Compute the rolling sum over a fixed-size window.
///
/// @param series Input series.
/// @param period Window size.
/// @returns Windowed sum for each position.
pub fn rolling_sum(series, period) {
__intrinsic_rolling_sum(series, period)
}
/// Compute the rolling arithmetic mean over a fixed-size window.
///
/// @param series Input series.
/// @param period Window size.
/// @returns Windowed mean for each position.
/// @see std::finance::indicators::moving_averages::sma
pub fn rolling_mean(series, period) {
__intrinsic_rolling_mean(series, period)
}
/// Compute the rolling standard deviation over a fixed-size window.
///
/// @param series Input series.
/// @param period Window size.
/// @returns Windowed standard deviation for each position.
pub fn rolling_std(series, period) {
__intrinsic_rolling_std(series, period)
}
/// Compute the rolling variance over a fixed-size window.
///
/// @param series Input series.
/// @param period Window size.
/// @returns Windowed variance for each position.
/// @see std::core::utils::rolling::rolling_std
pub fn rolling_variance(series, period) {
let std = rolling_std(series, period)
__intrinsic_vec_mul(std, std)
}
/// Apply a first-order linear recurrence across a series.
///
/// `y[t] = y[t-1] * decay + input[t]`
///
/// @param input Recurrence input term.
/// @param decay Decay factor applied to the previous output.
/// @param initial_value Seed value for the recurrence.
/// @returns Recurrence output series.
/// @see std::finance::indicators::moving_averages::ema
pub fn linear_recurrence(input, decay, initial_value) {
__intrinsic_linear_recurrence(input, decay, initial_value)
}