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use crate::{Forecast, ModelError};
/// A new, unfitted time series forecasting model.
pub trait Fit {
/// The type of the fitted model produced by the `fit` method.
type Fitted: Predict;
/// The type of error returned when fitting the model.
type Error: ModelError;
/// Fit the model to the training data.
fn fit(&self, y: &[f64]) -> Result<Self::Fitted, Self::Error>;
}
impl<F> Fit for Box<F>
where
F: Fit,
{
type Fitted = F::Fitted;
type Error = F::Error;
fn fit(&self, y: &[f64]) -> Result<Self::Fitted, Self::Error> {
(**self).fit(y)
}
}
/// A fitted time series forecasting model.
pub trait Predict {
/// The type of error returned when predicting with the model.
type Error: ModelError;
/// Calculate the in-sample predictions, storing the results in the provided
/// [`Forecast`] struct.
///
/// The predictions are point forecasts and optionally include
/// prediction intervals at the specified `level`.
///
/// `level` should be a float between 0 and 1 representing the
/// confidence level of the prediction intervals. If `None` then
/// no prediction intervals are returned.
///
/// # Errors
///
/// Any errors returned by the trend model are propagated.
fn predict_in_sample_inplace(
&self,
level: Option<f64>,
forecast: &mut Forecast,
) -> Result<(), Self::Error>;
/// Calculate the n-ahead predictions for the given horizon, storing the results in the
/// provided [`Forecast`] struct.
///
/// The predictions are point forecasts and optionally include
/// prediction intervals at the specified `level`.
///
/// `level` should be a float between 0 and 1 representing the
/// confidence level of the prediction intervals. If `None` then
/// no prediction intervals are returned.
///
/// # Errors
///
/// Any errors returned by the trend model are propagated.
fn predict_inplace(
&self,
horizon: usize,
level: Option<f64>,
forecast: &mut Forecast,
) -> Result<(), Self::Error>;
/// Return the number of training data points used to fit the model.
///
/// This is used for pre-allocating the in-sample forecasts.
fn training_data_size(&self) -> usize;
/// Return the n-ahead predictions for the given horizon.
///
/// The predictions are point forecasts and optionally include
/// prediction intervals at the specified `level`.
///
/// `level` should be a float between 0 and 1 representing the
/// confidence level of the prediction intervals. If `None` then
/// no prediction intervals are returned.
///
/// # Errors
///
/// Any errors returned by the trend model are propagated.
fn predict(
&self,
horizon: usize,
level: impl Into<Option<f64>>,
) -> Result<Forecast, Self::Error> {
let level = level.into();
let mut forecast = level
.map(|l| Forecast::with_capacity_and_level(horizon, l))
.unwrap_or_else(|| Forecast::with_capacity(horizon));
self.predict_inplace(horizon, level, &mut forecast)?;
Ok(forecast)
}
/// Return the in-sample predictions.
///
/// The predictions are point forecasts and optionally include
/// prediction intervals at the specified `level`.
///
/// `level` should be a float between 0 and 1 representing the
/// confidence level of the prediction intervals. If `None` then
/// no prediction intervals are returned.
///
/// # Errors
///
/// Any errors returned by the trend model are propagated.
fn predict_in_sample(&self, level: impl Into<Option<f64>>) -> Result<Forecast, Self::Error> {
let level = level.into();
let mut forecast = level
.map(|l| Forecast::with_capacity_and_level(self.training_data_size(), l))
.unwrap_or_else(|| Forecast::with_capacity(self.training_data_size()));
self.predict_in_sample_inplace(level, &mut forecast)?;
Ok(forecast)
}
}