//! Time series imputation methods
#![allow(non_snake_case)]
#![allow(dead_code)]
//!
//! This module provides specialized imputation strategies for time series data,
//! including seasonal decomposition, ARIMA-based imputation, state-space models, and Kalman filtering.

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use sklears_core::{
    error::{Result as SklResult, SklearsError},
    traits::{Estimator, Fit, Transform, Untrained},
    types::Float,
};
use std::collections::HashMap;

/// Seasonal Decomposition Imputer
///
/// Imputes missing values in time series data using seasonal decomposition.
/// Decomposes the series into trend, seasonal, and residual components,
/// then imputes missing values based on these components.
///
/// # Parameters
///
/// * `period` - Period of seasonality (e.g., 12 for monthly data with yearly cycles)
/// * `model` - Decomposition model ("additive" or "multiplicative")
/// * `extrapolate_trend` - Number of periods to extrapolate trend for missing values
/// * `two_sided` - Whether to use two-sided or one-sided trend estimation
/// * `missing_values` - The placeholder for missing values
///
/// # Examples
///
/// ```
/// use sklears_impute::SeasonalDecompositionImputer;
/// use sklears_core::traits::{Transform, Fit};
/// use scirs2_core::ndarray::array;
///
/// let X = array![[1.0], [2.0], [f64::NAN], [4.0], [5.0], [f64::NAN]];
///
/// let imputer = SeasonalDecompositionImputer::new()
///     .period(3)
///     .model("additive".to_string());
/// let fitted = imputer.fit(&X.view(), &()).unwrap();
/// let X_imputed = fitted.transform(&X.view()).unwrap();
/// ```
#[derive(Debug, Clone)]
pub struct SeasonalDecompositionImputer<S = Untrained> {
    state: S,
    period: usize,
    model: String,
    extrapolate_trend: usize,
    two_sided: bool,
    missing_values: f64,
}

/// Trained state for SeasonalDecompositionImputer
#[derive(Debug, Clone)]
pub struct SeasonalDecompositionImputerTrained {
    seasonal_components: HashMap<usize, Array1<f64>>,
    trend_model: TrendModel,
    residual_stats: ResidualStats,
    n_features_in_: usize,
}

/// Trend model for extrapolation
#[derive(Debug, Clone)]
pub struct TrendModel {
    coefficients: Array1<f64>,
    degree: usize,
}

/// Residual statistics
#[derive(Debug, Clone)]
pub struct ResidualStats {
    mean: f64,
    std: f64,
}

/// ARIMA Imputer
///
/// Uses ARIMA (AutoRegressive Integrated Moving Average) models to impute missing values.
/// Fits separate ARIMA models for each feature and uses them to predict missing values.
///
/// # Parameters
///
/// * `order` - ARIMA order (p, d, q) for autoregressive, differencing, and moving average terms
/// * `seasonal_order` - Seasonal ARIMA order (P, D, Q, s)
/// * `method` - Estimation method ("mle" or "css")
/// * `trend` - Trend component ("c" for constant, "t" for linear trend, "ct" for both)
/// * `missing_values` - The placeholder for missing values
///
/// # Examples
///
/// ```
/// use sklears_impute::ARIMAImputer;
/// use sklears_core::traits::{Transform, Fit};
/// use scirs2_core::ndarray::array;
///
/// let X = array![[1.0], [2.0], [f64::NAN], [4.0], [5.0], [f64::NAN]];
///
/// let imputer = ARIMAImputer::new()
///     .order((1, 1, 1))
///     .seasonal_order((0, 0, 0, 0));
/// let fitted = imputer.fit(&X.view(), &()).unwrap();
/// let X_imputed = fitted.transform(&X.view()).unwrap();
/// ```
#[derive(Debug, Clone)]
pub struct ARIMAImputer<S = Untrained> {
    state: S,
    order: (usize, usize, usize),
    seasonal_order: (usize, usize, usize, usize),
    method: String,
    trend: String,
    missing_values: f64,
}

/// Trained state for ARIMAImputer
#[derive(Debug, Clone)]
pub struct ARIMAImputerTrained {
    models: Vec<ARIMAModel>,
    n_features_in_: usize,
}

/// ARIMA model for a single time series
#[derive(Debug, Clone)]
pub struct ARIMAModel {
    ar_params: Array1<f64>,
    ma_params: Array1<f64>,
    trend_params: Array1<f64>,
    sigma2: f64,
    order: (usize, usize, usize),
    seasonal_order: (usize, usize, usize, usize),
}

/// Kalman Filter Imputer
///
/// Uses Kalman filtering for state-space model based imputation.
/// Models the time series as a state-space system and uses the Kalman filter
/// to estimate missing values.
///
/// # Parameters
///
/// * `state_dim` - Dimensionality of state vector
/// * `observation_noise` - Observation noise variance
/// * `process_noise` - Process noise variance
/// * `initial_state_variance` - Initial state covariance
/// * `state_transition` - State transition model ("local_level", "local_trend", "seasonal")
/// * `missing_values` - The placeholder for missing values
///
/// # Examples
///
/// ```
/// use sklears_impute::KalmanFilterImputer;
/// use sklears_core::traits::{Transform, Fit};
/// use scirs2_core::ndarray::array;
///
/// let X = array![[1.0], [2.0], [f64::NAN], [4.0], [5.0], [f64::NAN]];
///
/// let imputer = KalmanFilterImputer::new()
///     .state_dim(2)
///     .state_transition("local_trend".to_string());
/// let fitted = imputer.fit(&X.view(), &()).unwrap();
/// let X_imputed = fitted.transform(&X.view()).unwrap();
/// ```
#[derive(Debug, Clone)]
pub struct KalmanFilterImputer<S = Untrained> {
    state: S,
    state_dim: usize,
    observation_noise: f64,
    process_noise: f64,
    initial_state_variance: f64,
    state_transition: String,
    missing_values: f64,
}

/// Trained state for KalmanFilterImputer
#[derive(Debug, Clone)]
pub struct KalmanFilterImputerTrained {
    state_space_models: Vec<StateSpaceModel>,
    n_features_in_: usize,
}

/// State-space model for Kalman filtering
#[derive(Debug, Clone)]
pub struct StateSpaceModel {
    transition_matrix: Array2<f64>,
    observation_matrix: Array2<f64>,
    process_covariance: Array2<f64>,
    observation_covariance: Array2<f64>,
    initial_state: Array1<f64>,
    initial_covariance: Array2<f64>,
}

/// State-Space Model Imputer
///
/// General state-space model imputer that can handle various structural time series models.
/// Provides flexibility to specify custom state-space representations.
///
/// # Parameters
///
/// * `model_type` - Type of structural model ("local_level", "local_trend", "seasonal", "custom")
/// * `seasonal_periods` - Periods for seasonal components
/// * `estimation_method` - Parameter estimation method ("mle", "em")
/// * `max_iter` - Maximum iterations for parameter estimation
/// * `tolerance` - Convergence tolerance
/// * `missing_values` - The placeholder for missing values
#[derive(Debug, Clone)]
pub struct StateSpaceImputer<S = Untrained> {
    state: S,
    model_type: String,
    seasonal_periods: Vec<usize>,
    estimation_method: String,
    max_iter: usize,
    tolerance: f64,
    missing_values: f64,
}

/// Trained state for StateSpaceImputer
#[derive(Debug, Clone)]
pub struct StateSpaceImputerTrained {
    models: Vec<StructuralTimeSeriesModel>,
    n_features_in_: usize,
}

/// Structural time series model
#[derive(Debug, Clone)]
pub struct StructuralTimeSeriesModel {
    level_variance: f64,
    trend_variance: f64,
    seasonal_variances: Vec<f64>,
    observation_variance: f64,
    initial_components: ComponentState,
}

/// Component state for structural model
#[derive(Debug, Clone)]
pub struct ComponentState {
    level: f64,
    trend: f64,
    seasonal: Vec<f64>,
}

// SeasonalDecompositionImputer implementation

impl SeasonalDecompositionImputer<Untrained> {
    /// Create a new SeasonalDecompositionImputer instance
    pub fn new() -> Self {
        Self {
            state: Untrained,
            period: 12,
            model: "additive".to_string(),
            extrapolate_trend: 1,
            two_sided: true,
            missing_values: f64::NAN,
        }
    }

    /// Set the seasonal period
    pub fn period(mut self, period: usize) -> Self {
        self.period = period;
        self
    }

    /// Set the decomposition model
    pub fn model(mut self, model: String) -> Self {
        self.model = model;
        self
    }

    /// Set trend extrapolation periods
    pub fn extrapolate_trend(mut self, extrapolate_trend: usize) -> Self {
        self.extrapolate_trend = extrapolate_trend;
        self
    }

    /// Set whether to use two-sided trend estimation
    pub fn two_sided(mut self, two_sided: bool) -> Self {
        self.two_sided = two_sided;
        self
    }

    /// Set the missing values placeholder
    pub fn missing_values(mut self, missing_values: f64) -> Self {
        self.missing_values = missing_values;
        self
    }

    fn is_missing(&self, value: f64) -> bool {
        if self.missing_values.is_nan() {
            value.is_nan()
        } else {
            (value - self.missing_values).abs() < f64::EPSILON
        }
    }
}

impl Default for SeasonalDecompositionImputer<Untrained> {
    fn default() -> Self {
        Self::new()
    }
}

impl Estimator for SeasonalDecompositionImputer<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = Float;

    fn config(&self) -> &Self::Config {
        &()
    }
}

impl Fit<ArrayView2<'_, Float>, ()> for SeasonalDecompositionImputer<Untrained> {
    type Fitted = SeasonalDecompositionImputer<SeasonalDecompositionImputerTrained>;

    #[allow(non_snake_case)]
    fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted> {
        let X = X.mapv(|x| x);
        let (n_samples, n_features) = X.dim();

        if n_samples == 0 || n_features == 0 {
            return Err(SklearsError::InvalidInput("Empty dataset".to_string()));
        }

        if n_samples < self.period * 2 {
            return Err(SklearsError::InvalidInput(
                "Need at least 2 full periods for seasonal decomposition".to_string(),
            ));
        }

        let mut seasonal_components = HashMap::new();
        let mut trend_model = TrendModel {
            coefficients: Array1::zeros(2),
            degree: 1,
        };
        let mut residual_stats = ResidualStats {
            mean: 0.0,
            std: 1.0,
        };

        // Process each feature separately
        for feature_idx in 0..n_features {
            let series = X.column(feature_idx).to_owned();

            // Extract non-missing values for decomposition
            let non_missing_indices: Vec<usize> = (0..n_samples)
                .filter(|&i| !self.is_missing(series[i]))
                .collect();

            if non_missing_indices.len() < self.period {
                continue; // Skip features with too few observations
            }

            // Perform seasonal decomposition
            let decomposition = self.seasonal_decompose(&series, &non_missing_indices)?;
            seasonal_components.insert(feature_idx, decomposition.seasonal);

            // Fit trend model
            if feature_idx == 0 {
                trend_model = self.fit_trend_model(&decomposition.trend, &non_missing_indices)?;
                residual_stats = self.compute_residual_stats(&decomposition.residual);
            }
        }

        Ok(SeasonalDecompositionImputer {
            state: SeasonalDecompositionImputerTrained {
                seasonal_components,
                trend_model,
                residual_stats,
                n_features_in_: n_features,
            },
            period: self.period,
            model: self.model,
            extrapolate_trend: self.extrapolate_trend,
            two_sided: self.two_sided,
            missing_values: self.missing_values,
        })
    }
}

impl Transform<ArrayView2<'_, Float>, Array2<Float>>
    for SeasonalDecompositionImputer<SeasonalDecompositionImputerTrained>
{
    #[allow(non_snake_case)]
    fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>> {
        let X = X.mapv(|x| x);
        let (n_samples, n_features) = X.dim();

        if n_features != self.state.n_features_in_ {
            return Err(SklearsError::InvalidInput(format!(
                "Number of features {} does not match training features {}",
                n_features, self.state.n_features_in_
            )));
        }

        let mut X_imputed = X.clone();

        for feature_idx in 0..n_features {
            if let Some(seasonal_component) = self.state.seasonal_components.get(&feature_idx) {
                for i in 0..n_samples {
                    if self.is_missing(X_imputed[[i, feature_idx]]) {
                        // Impute using seasonal + trend + residual
                        let seasonal_value = seasonal_component[i % self.period];
                        let trend_value = self.extrapolate_trend_value(i);
                        let residual_value = self.state.residual_stats.mean;

                        let imputed_value = match self.model.as_str() {
                            "multiplicative" => {
                                trend_value * seasonal_value * (1.0 + residual_value)
                            }
                            _ => trend_value + seasonal_value + residual_value,
                        };

                        X_imputed[[i, feature_idx]] = imputed_value;
                    }
                }
            } else {
                // Fallback to simple trend extrapolation
                for i in 0..n_samples {
                    if self.is_missing(X_imputed[[i, feature_idx]]) {
                        X_imputed[[i, feature_idx]] = self.extrapolate_trend_value(i);
                    }
                }
            }
        }

        Ok(X_imputed.mapv(|x| x as Float))
    }
}

impl SeasonalDecompositionImputer<Untrained> {
    fn seasonal_decompose(
        &self,
        series: &Array1<f64>,
        non_missing_indices: &[usize],
    ) -> SklResult<SeasonalDecomposition> {
        let n = series.len();
        let mut trend = Array1::zeros(n);
        let mut seasonal = Array1::zeros(self.period);
        let mut residual = Array1::zeros(n);

        // Extract non-missing values
        let values: Vec<f64> = non_missing_indices.iter().map(|&i| series[i]).collect();

        // Simple moving average for trend estimation
        let window = self.period;
        for (idx, &i) in non_missing_indices.iter().enumerate() {
            if idx >= window / 2 && idx < values.len() - window / 2 {
                let start = idx.saturating_sub(window / 2);
                let end = (idx + window / 2 + 1).min(values.len());
                let mean = values[start..end].iter().sum::<f64>() / (end - start) as f64;
                trend[i] = mean;
            }
        }

        // Fill trend endpoints with linear extrapolation
        self.extrapolate_trend_endpoints(&mut trend, non_missing_indices);

        // Compute seasonal components
        let mut seasonal_sums = vec![0.0; self.period];
        let mut seasonal_counts = vec![0; self.period];

        for &i in non_missing_indices {
            let detrended = match self.model.as_str() {
                "multiplicative" => {
                    if trend[i] != 0.0 {
                        series[i] / trend[i]
                    } else {
                        1.0
                    }
                }
                _ => series[i] - trend[i],
            };

            let season_idx = i % self.period;
            seasonal_sums[season_idx] += detrended;
            seasonal_counts[season_idx] += 1;
        }

        for (i, &count) in seasonal_counts.iter().enumerate() {
            if count > 0 {
                seasonal[i] = seasonal_sums[i] / count as f64;
            }
        }

        // Adjust seasonal components to sum to zero (additive) or product to 1 (multiplicative)
        match self.model.as_str() {
            "multiplicative" => {
                let seasonal_mean = seasonal.mean().unwrap_or(1.0);
                if seasonal_mean != 0.0 {
                    seasonal /= seasonal_mean;
                }
            }
            _ => {
                let seasonal_mean = seasonal.mean().unwrap_or(0.0);
                seasonal -= seasonal_mean;
            }
        }

        // Compute residuals
        for &i in non_missing_indices {
            let season_idx = i % self.period;
            residual[i] = match self.model.as_str() {
                "multiplicative" => {
                    if trend[i] != 0.0 && seasonal[season_idx] != 0.0 {
                        (series[i] / trend[i] / seasonal[season_idx]) - 1.0
                    } else {
                        0.0
                    }
                }
                _ => series[i] - trend[i] - seasonal[season_idx],
            };
        }

        Ok(SeasonalDecomposition {
            trend,
            seasonal,
            residual,
        })
    }

    fn extrapolate_trend_endpoints(&self, trend: &mut Array1<f64>, non_missing_indices: &[usize]) {
        if non_missing_indices.len() < 2 {
            return;
        }

        // Find first and last non-zero trend values
        let mut first_valid = None;
        let mut last_valid = None;

        for &i in non_missing_indices {
            if trend[i] != 0.0 {
                if first_valid.is_none() {
                    first_valid = Some(i);
                }
                last_valid = Some(i);
            }
        }

        if let (Some(first), Some(last)) = (first_valid, last_valid) {
            if first > 0 {
                // Extrapolate backwards
                let slope = if last > first {
                    (trend[last] - trend[first]) / (last - first) as f64
                } else {
                    0.0
                };

                for i in 0..first {
                    trend[i] = trend[first] - slope * (first - i) as f64;
                }
            }

            if last < trend.len() - 1 {
                // Extrapolate forwards
                let slope = if last > first {
                    (trend[last] - trend[first]) / (last - first) as f64
                } else {
                    0.0
                };

                for i in (last + 1)..trend.len() {
                    trend[i] = trend[last] + slope * (i - last) as f64;
                }
            }
        }
    }

    fn fit_trend_model(
        &self,
        trend: &Array1<f64>,
        non_missing_indices: &[usize],
    ) -> SklResult<TrendModel> {
        if non_missing_indices.len() < 2 {
            return Ok(TrendModel {
                coefficients: Array1::zeros(2),
                degree: 1,
            });
        }

        // Simple linear regression for trend
        let n = non_missing_indices.len() as f64;
        let sum_x = non_missing_indices.iter().map(|&i| i as f64).sum::<f64>();
        let sum_y = non_missing_indices.iter().map(|&i| trend[i]).sum::<f64>();
        let sum_xy = non_missing_indices
            .iter()
            .map(|&i| i as f64 * trend[i])
            .sum::<f64>();
        let sum_xx = non_missing_indices
            .iter()
            .map(|&i| (i as f64).powi(2))
            .sum::<f64>();

        let denominator = n * sum_xx - sum_x * sum_x;
        let slope = if denominator != 0.0 {
            (n * sum_xy - sum_x * sum_y) / denominator
        } else {
            0.0
        };
        let intercept = (sum_y - slope * sum_x) / n;

        Ok(TrendModel {
            coefficients: Array1::from_vec(vec![intercept, slope]),
            degree: 1,
        })
    }

    fn compute_residual_stats(&self, residual: &Array1<f64>) -> ResidualStats {
        let non_zero_residuals: Vec<f64> = residual
            .iter()
            .filter(|&&x| x != 0.0 && !x.is_nan())
            .cloned()
            .collect();

        if non_zero_residuals.is_empty() {
            return ResidualStats {
                mean: 0.0,
                std: 1.0,
            };
        }

        let mean = non_zero_residuals.iter().sum::<f64>() / non_zero_residuals.len() as f64;
        let variance = non_zero_residuals
            .iter()
            .map(|&x| (x - mean).powi(2))
            .sum::<f64>()
            / (non_zero_residuals.len() - 1).max(1) as f64;
        let std = variance.sqrt().max(1e-8);

        ResidualStats { mean, std }
    }
}

impl SeasonalDecompositionImputer<SeasonalDecompositionImputerTrained> {
    fn is_missing(&self, value: f64) -> bool {
        if self.missing_values.is_nan() {
            value.is_nan()
        } else {
            (value - self.missing_values).abs() < f64::EPSILON
        }
    }

    fn extrapolate_trend_value(&self, time_index: usize) -> f64 {
        let coeffs = &self.state.trend_model.coefficients;
        if coeffs.len() >= 2 {
            coeffs[0] + coeffs[1] * (time_index as f64)
        } else {
            coeffs.first().cloned().unwrap_or(0.0)
        }
    }
}

/// Seasonal decomposition result
#[derive(Debug, Clone)]
struct SeasonalDecomposition {
    trend: Array1<f64>,
    seasonal: Array1<f64>,
    residual: Array1<f64>,
}

// ARIMAImputer implementation

impl ARIMAImputer<Untrained> {
    /// Create a new ARIMAImputer instance
    pub fn new() -> Self {
        Self {
            state: Untrained,
            order: (1, 1, 1),
            seasonal_order: (0, 0, 0, 0),
            method: "mle".to_string(),
            trend: "c".to_string(),
            missing_values: f64::NAN,
        }
    }

    /// Set the ARIMA order (p, d, q)
    pub fn order(mut self, order: (usize, usize, usize)) -> Self {
        self.order = order;
        self
    }

    /// Set the seasonal ARIMA order (P, D, Q, s)
    pub fn seasonal_order(mut self, seasonal_order: (usize, usize, usize, usize)) -> Self {
        self.seasonal_order = seasonal_order;
        self
    }

    /// Set the estimation method
    pub fn method(mut self, method: String) -> Self {
        self.method = method;
        self
    }

    /// Set the trend component
    pub fn trend(mut self, trend: String) -> Self {
        self.trend = trend;
        self
    }

    /// Set the missing values placeholder
    pub fn missing_values(mut self, missing_values: f64) -> Self {
        self.missing_values = missing_values;
        self
    }

    fn is_missing(&self, value: f64) -> bool {
        if self.missing_values.is_nan() {
            value.is_nan()
        } else {
            (value - self.missing_values).abs() < f64::EPSILON
        }
    }
}

impl Default for ARIMAImputer<Untrained> {
    fn default() -> Self {
        Self::new()
    }
}

impl Estimator for ARIMAImputer<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = Float;

    fn config(&self) -> &Self::Config {
        &()
    }
}

impl Fit<ArrayView2<'_, Float>, ()> for ARIMAImputer<Untrained> {
    type Fitted = ARIMAImputer<ARIMAImputerTrained>;

    #[allow(non_snake_case)]
    fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted> {
        let X = X.mapv(|x| x);
        let (n_samples, n_features) = X.dim();

        if n_samples == 0 || n_features == 0 {
            return Err(SklearsError::InvalidInput("Empty dataset".to_string()));
        }

        let mut models = Vec::new();

        // Fit ARIMA model for each feature
        for feature_idx in 0..n_features {
            let series = X.column(feature_idx).to_owned();
            let model = self.fit_arima_model(&series)?;
            models.push(model);
        }

        Ok(ARIMAImputer {
            state: ARIMAImputerTrained {
                models,
                n_features_in_: n_features,
            },
            order: self.order,
            seasonal_order: self.seasonal_order,
            method: self.method,
            trend: self.trend,
            missing_values: self.missing_values,
        })
    }
}

impl Transform<ArrayView2<'_, Float>, Array2<Float>> for ARIMAImputer<ARIMAImputerTrained> {
    #[allow(non_snake_case)]
    fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>> {
        let X = X.mapv(|x| x);
        let (n_samples, n_features) = X.dim();

        if n_features != self.state.n_features_in_ {
            return Err(SklearsError::InvalidInput(format!(
                "Number of features {} does not match training features {}",
                n_features, self.state.n_features_in_
            )));
        }

        let mut X_imputed = X.clone();

        for feature_idx in 0..n_features {
            let model = &self.state.models[feature_idx];
            let series = X_imputed.column(feature_idx).to_owned();

            // Find missing values and impute them
            for i in 0..n_samples {
                if self.is_missing(X_imputed[[i, feature_idx]]) {
                    let imputed_value = self.predict_arima_value(&series, i, model)?;
                    X_imputed[[i, feature_idx]] = imputed_value;
                }
            }
        }

        Ok(X_imputed.mapv(|x| x as Float))
    }
}

impl ARIMAImputer<Untrained> {
    fn fit_arima_model(&self, series: &Array1<f64>) -> SklResult<ARIMAModel> {
        let (p, d, q) = self.order;

        // Extract non-missing values
        let non_missing_values: Vec<f64> = series
            .iter()
            .filter(|&&x| !self.is_missing(x))
            .cloned()
            .collect();

        if non_missing_values.len() < p + q + 1 {
            // Not enough data for ARIMA, return simple model
            return Ok(ARIMAModel {
                ar_params: Array1::zeros(p),
                ma_params: Array1::zeros(q),
                trend_params: Array1::zeros(1),
                sigma2: 1.0,
                order: self.order,
                seasonal_order: self.seasonal_order,
            });
        }

        // Difference the series if d > 0
        let mut differenced = non_missing_values.clone();
        for _ in 0..d {
            differenced = differenced.windows(2).map(|w| w[1] - w[0]).collect();
        }

        if differenced.len() < p + q + 1 {
            return Ok(ARIMAModel {
                ar_params: Array1::zeros(p),
                ma_params: Array1::zeros(q),
                trend_params: Array1::zeros(1),
                sigma2: 1.0,
                order: self.order,
                seasonal_order: self.seasonal_order,
            });
        }

        // Simplified ARIMA parameter estimation using method of moments
        let (ar_params, ma_params, sigma2) = self.estimate_arima_parameters(&differenced, p, q)?;

        // Trend parameters (simplified)
        let mean = differenced.iter().sum::<f64>() / differenced.len() as f64;
        let trend_params = Array1::from_vec(vec![mean]);

        Ok(ARIMAModel {
            ar_params,
            ma_params,
            trend_params,
            sigma2,
            order: self.order,
            seasonal_order: self.seasonal_order,
        })
    }

    fn estimate_arima_parameters(
        &self,
        data: &[f64],
        p: usize,
        q: usize,
    ) -> SklResult<(Array1<f64>, Array1<f64>, f64)> {
        let n = data.len();
        if n < p + q + 1 {
            return Ok((Array1::zeros(p), Array1::zeros(q), 1.0));
        }

        // Simplified estimation using Yule-Walker equations for AR part
        let mut ar_params = Array1::zeros(p);
        if p > 0 && n > p {
            // Compute autocorrelations
            let mean = data.iter().sum::<f64>() / n as f64;
            let mut autocorrs = Vec::new();

            for lag in 0..=p {
                let mut sum = 0.0;
                let mut count = 0;
                for i in lag..n {
                    sum += (data[i] - mean) * (data[i - lag] - mean);
                    count += 1;
                }
                if count > 0 {
                    autocorrs.push(sum / count as f64);
                } else {
                    autocorrs.push(0.0);
                }
            }

            // Yule-Walker equations (simplified)
            if autocorrs[0] != 0.0 {
                for i in 0..p {
                    if i + 1 < autocorrs.len() {
                        ar_params[i] = autocorrs[i + 1] / autocorrs[0];
                    }
                }
            }
        }

        // MA parameters (simplified - set to zero for now)
        let ma_params = Array1::zeros(q);

        // Estimate sigma2
        let mut residual_sum = 0.0;
        let mut residual_count = 0;

        for i in p..n {
            let mut prediction = 0.0;
            for j in 0..p {
                if i > j {
                    prediction += ar_params[j] * data[i - j - 1];
                }
            }
            let residual = data[i] - prediction;
            residual_sum += residual * residual;
            residual_count += 1;
        }

        let sigma2 = if residual_count > 0 {
            residual_sum / residual_count as f64
        } else {
            1.0
        };

        Ok((ar_params, ma_params, sigma2.max(1e-8)))
    }
}

impl ARIMAImputer<ARIMAImputerTrained> {
    fn is_missing(&self, value: f64) -> bool {
        if self.missing_values.is_nan() {
            value.is_nan()
        } else {
            (value - self.missing_values).abs() < f64::EPSILON
        }
    }

    fn predict_arima_value(
        &self,
        series: &Array1<f64>,
        missing_index: usize,
        model: &ARIMAModel,
    ) -> SklResult<f64> {
        let (p, _d, _q) = model.order;

        // Find the most recent non-missing values
        let mut recent_values = Vec::new();
        for i in (0..missing_index).rev() {
            if !self.is_missing(series[i]) {
                recent_values.push(series[i]);
                if recent_values.len() >= p {
                    break;
                }
            }
        }

        if recent_values.is_empty() {
            // No history available, use trend
            return Ok(model.trend_params.first().cloned().unwrap_or(0.0));
        }

        // Reverse to get chronological order
        recent_values.reverse();

        // AR prediction
        let mut prediction = model.trend_params.first().cloned().unwrap_or(0.0);
        for (i, &ar_coef) in model.ar_params.iter().enumerate() {
            if i < recent_values.len() {
                prediction += ar_coef * recent_values[recent_values.len() - 1 - i];
            }
        }

        Ok(prediction)
    }
}

// Placeholder implementations for KalmanFilterImputer and StateSpaceImputer
// (These would require more complex implementation of Kalman filtering algorithms)

impl KalmanFilterImputer<Untrained> {
    /// Create a new KalmanFilterImputer instance
    pub fn new() -> Self {
        Self {
            state: Untrained,
            state_dim: 2,
            observation_noise: 1.0,
            process_noise: 1.0,
            initial_state_variance: 1.0,
            state_transition: "local_level".to_string(),
            missing_values: f64::NAN,
        }
    }

    /// Set the state dimension
    pub fn state_dim(mut self, state_dim: usize) -> Self {
        self.state_dim = state_dim;
        self
    }

    /// Set the observation noise variance
    pub fn observation_noise(mut self, observation_noise: f64) -> Self {
        self.observation_noise = observation_noise;
        self
    }

    /// Set the process noise variance
    pub fn process_noise(mut self, process_noise: f64) -> Self {
        self.process_noise = process_noise;
        self
    }

    /// Set the initial state variance
    pub fn initial_state_variance(mut self, initial_state_variance: f64) -> Self {
        self.initial_state_variance = initial_state_variance;
        self
    }

    /// Set the state transition model
    pub fn state_transition(mut self, state_transition: String) -> Self {
        self.state_transition = state_transition;
        self
    }

    /// Set the missing values placeholder
    pub fn missing_values(mut self, missing_values: f64) -> Self {
        self.missing_values = missing_values;
        self
    }
}

impl Default for KalmanFilterImputer<Untrained> {
    fn default() -> Self {
        Self::new()
    }
}

impl Estimator for KalmanFilterImputer<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = Float;

    fn config(&self) -> &Self::Config {
        &()
    }
}

impl Fit<ArrayView2<'_, Float>, ()> for KalmanFilterImputer<Untrained> {
    type Fitted = KalmanFilterImputer<KalmanFilterImputerTrained>;

    #[allow(non_snake_case)]
    fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted> {
        let X = X.mapv(|x| x);
        let (_n_samples, n_features) = X.dim();

        // Simplified implementation - in practice would fit state-space models
        let state_space_models = vec![
            StateSpaceModel {
                transition_matrix: Array2::eye(self.state_dim),
                observation_matrix: Array2::from_shape_vec((1, self.state_dim), vec![1.0, 0.0])
                    .unwrap_or_else(|_| Array2::zeros((1, self.state_dim))),
                process_covariance: Array2::eye(self.state_dim) * self.process_noise,
                observation_covariance: Array2::from_shape_vec(
                    (1, 1),
                    vec![self.observation_noise]
                )
                .unwrap_or_else(|_| Array2::zeros((1, 1))),
                initial_state: Array1::zeros(self.state_dim),
                initial_covariance: Array2::eye(self.state_dim) * self.initial_state_variance,
            };
            n_features
        ];

        Ok(KalmanFilterImputer {
            state: KalmanFilterImputerTrained {
                state_space_models,
                n_features_in_: n_features,
            },
            state_dim: self.state_dim,
            observation_noise: self.observation_noise,
            process_noise: self.process_noise,
            initial_state_variance: self.initial_state_variance,
            state_transition: self.state_transition,
            missing_values: self.missing_values,
        })
    }
}

impl Transform<ArrayView2<'_, Float>, Array2<Float>>
    for KalmanFilterImputer<KalmanFilterImputerTrained>
{
    #[allow(non_snake_case)]
    fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>> {
        let X = X.mapv(|x| x);
        let (n_samples, n_features) = X.dim();

        if n_features != self.state.n_features_in_ {
            return Err(SklearsError::InvalidInput(format!(
                "Number of features {} does not match training features {}",
                n_features, self.state.n_features_in_
            )));
        }

        let mut X_imputed = X.clone();

        // Simplified implementation - would normally run Kalman filter
        for feature_idx in 0..n_features {
            let series = X_imputed.column(feature_idx).to_owned();
            let mut last_observed = 0.0;
            let mut has_observed = false;

            // Forward pass to find last observed value
            for i in 0..n_samples {
                if !self.is_missing(series[i]) {
                    last_observed = series[i];
                    has_observed = true;
                } else if has_observed {
                    X_imputed[[i, feature_idx]] = last_observed; // Simple carry-forward
                }
            }

            // Backward pass for values before first observation
            if has_observed {
                for i in 0..n_samples {
                    if self.is_missing(X_imputed[[i, feature_idx]]) {
                        X_imputed[[i, feature_idx]] = last_observed;
                    } else {
                        break;
                    }
                }
            }
        }

        Ok(X_imputed.mapv(|x| x as Float))
    }
}

impl KalmanFilterImputer<KalmanFilterImputerTrained> {
    fn is_missing(&self, value: f64) -> bool {
        if self.missing_values.is_nan() {
            value.is_nan()
        } else {
            (value - self.missing_values).abs() < f64::EPSILON
        }
    }
}
