Trait tea_rolling::RollingValidRegBinary

source · 
pub trait RollingValidRegBinary<T: IsNone>: Vec1View<T> {
    // Provided methods
    fn ts_vregx_alpha_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
        out: Option<O::UninitRefMut<'_>>,
    ) -> Option<O>
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_alpha<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
    ) -> O
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_beta_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
        out: Option<O::UninitRefMut<'_>>,
    ) -> Option<O>
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_beta<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
    ) -> O
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_resid_mean_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
        out: Option<O::UninitRefMut<'_>>,
    ) -> Option<O>
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_resid_mean<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
    ) -> O
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_resid_std_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
        out: Option<O::UninitRefMut<'_>>,
    ) -> Option<O>
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_resid_std<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
    ) -> O
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_resid_skew_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
        out: Option<O::UninitRefMut<'_>>,
    ) -> Option<O>
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_resid_skew<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
    ) -> O
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vregx_all<O: Vec1<(U, U, U)>, U, V2: Vec1View<T2>, T2: IsNone>(
        &self,
        other: &V2,
        window: usize,
        min_periods: Option<usize>,
    ) -> O
       where T::Inner: Number,
             T2::Inner: Number,
             f64: Cast<U> { ... }
}
Expand description

Trait for rolling window regression operations on valid (non-None) elements with two input vectors.

Provided Methods§

source

fn ts_vregx_alpha_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, out: Option<O::UninitRefMut<'_>>, ) -> Option<O>
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling regression alpha (intercept) for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling regression alpha values.

source

fn ts_vregx_alpha<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, ) -> O
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling regression alpha (intercept) for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling regression alpha values.

source

fn ts_vregx_beta_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, out: Option<O::UninitRefMut<'_>>, ) -> Option<O>
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling regression beta (slope) for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling regression beta values.

source

fn ts_vregx_beta<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, ) -> O
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling regression beta (slope) for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling regression beta values.

source

fn ts_vregx_resid_mean_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, out: Option<O::UninitRefMut<'_>>, ) -> Option<O>
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling mean of regression residuals for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling mean of regression residuals.

source

fn ts_vregx_resid_mean<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, ) -> O
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling mean of regression residuals for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling mean of regression residuals.

source

fn ts_vregx_resid_std_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, out: Option<O::UninitRefMut<'_>>, ) -> Option<O>
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling standard deviation of regression residuals for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling standard deviation of regression residuals.

source

fn ts_vregx_resid_std<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, ) -> O
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling standard deviation of regression residuals for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling standard deviation of regression residuals.

source

fn ts_vregx_resid_skew_to<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, out: Option<O::UninitRefMut<'_>>, ) -> Option<O>
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling skewness of regression residuals for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling skewness of regression residuals.

source

fn ts_vregx_resid_skew<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, ) -> O
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates the rolling skewness of regression residuals for valid elements within a window.

§Arguments
  • other - The second input vector for the regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling skewness of regression residuals.

source

fn ts_vregx_all<O: Vec1<(U, U, U)>, U, V2: Vec1View<T2>, T2: IsNone>( &self, other: &V2, window: usize, min_periods: Option<usize>, ) -> O
where T::Inner: Number, T2::Inner: Number, f64: Cast<U>,

Calculates rolling regression statistics for two vectors.

This function computes rolling regression statistics (alpha, beta, and sum of squared errors) for two input vectors over a specified window size.

§Arguments
  • other - The second input vector for regression.
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations required to have a value; defaults to window / 2.
§Type Parameters
  • O - The output vector type, must implement Vec1<(U, U, U)>.
  • U - The type of the output elements.
  • V2 - The type of the second input vector, must implement Vec1View<T2>.
  • T2 - The element type of the second input vector, must implement IsNone.
§Returns

Returns a vector of tuples (alpha, beta, sse) where:

  • alpha is the y-intercept of the regression line.
  • beta is the slope of the regression line.
  • sse is the sum of squared errors.
§Notes
  • The function uses a rolling window approach to calculate regression statistics.
  • NaN values are returned for windows with insufficient observations.
  • The calculation assumes that T::Inner and T2::Inner implement Number.
  • The output type U must be able to be cast from f64.

Object Safety§

This trait is not object safe.

Implementors§