Struct TrendMetrics 

pub struct TrendMetrics {
    pub slope: Decimal,
    pub intercept: Decimal,
    pub r_squared: Decimal,
    pub moving_average: Vec<Point2D>,
}

Represents key metrics for analyzing trends within a dataset or curve.

Overview

This structure stores comprehensive metrics that describe the linear relationship and trend behavior in a dataset. It provides statistical values derived from linear regression analysis along with smoothed data points for visualization and further analysis.

Fields

• slope: The rate of change in the dependent variable (y-axis) with respect to the independent variable (x-axis), calculated from linear regression.
• intercept: The y-coordinate where the regression line intersects the y-axis, representing the value when x = 0.
• r_squared: The coefficient of determination that measures how well the data fits the regression model, with values closer to 1 indicating a better fit.
• moving_average: A series of smoothed data points derived from the original dataset to reduce noise and highlight the underlying trend pattern.

Purpose

TrendMetrics is particularly useful for:

• Analyzing time-series data in financial markets
• Evaluating performance trends over time
• Quantifying the strength and direction of relationships between variables
• Providing a basis for predictive modeling and forecasting

This structure is a key component of the curve analysis module and works alongside other metric types to provide comprehensive statistical analysis.

Fields

slope: Decimal

The slope coefficient of the linear regression line, indicating the rate and direction of change in the dataset.

intercept: Decimal

The y-intercept of the regression line, showing the baseline value when the independent variable equals zero.

r_squared: Decimal

The coefficient of determination (R²), measuring how well the regression line fits the actual data points. Values range from 0 to 1, where 1 represents a perfect fit.

moving_average: Vec<Point2D>

A collection of points representing the smoothed version of the original data, calculated using moving average techniques to reduce noise and highlight the underlying trend.

Trait Implementations

impl Clone for TrendMetrics

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl ComposeSchema for TrendMetrics

fn compose(generics: Vec<RefOr<Schema>>) -> RefOr<Schema>

impl Debug for TrendMetrics

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

Formats the value using the given formatter.

impl Display for TrendMetrics

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

Formats the value using the given formatter.

impl Serialize for TrendMetrics

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl ToSchema for TrendMetrics

fn name() -> Cow<'static, str>

Return name of the schema.

fn schemas(schemas: &mut Vec<(String, RefOr<Schema>)>)

Implement reference utoipa::openapi::schema::Schemas for this type.