torsh-backend 0.1.2

//! Performance Prediction Module
//!
//! This module provides comprehensive performance prediction capabilities for CUDA memory optimization,
//! including time series forecasting, trend analysis, feature extraction, and accuracy tracking.

use scirs2_core::ndarray::{Array1, Array2};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

/// Performance predictor for optimization strategies
#[derive(Debug)]
pub struct PerformancePredictor {
    /// Prediction models indexed by strategy name
    models: HashMap<String, PredictionModel>,
    /// Historical performance data for training
    historical_data: VecDeque<HistoricalPerformance>,
    /// Feature extractors for performance data
    feature_extractors: Vec<PerformanceFeatureExtractor>,
    /// Prediction accuracy tracker
    accuracy_tracker: PredictionAccuracyTracker,
    /// Predictor configuration
    config: PredictorConfig,
    /// Model ensemble for combined predictions
    ensemble: ModelEnsemble,
    /// Real-time feature processor
    feature_processor: FeatureProcessor,
    /// Prediction cache for performance optimization
    prediction_cache: Arc<RwLock<HashMap<String, CachedPrediction>>>,
    /// Cross-validation manager
    cross_validator: CrossValidationManager,
    /// Anomaly detection for prediction quality
    anomaly_detector: PredictionAnomalyDetector,
    /// Auto-ML pipeline for model optimization
    auto_ml: AutoMLPipeline,
}

/// Individual prediction model
#[derive(Debug, Clone)]
pub struct PredictionModel {
    /// Model identifier
    pub id: String,
    /// Model type and algorithm
    pub model_type: PredictionModelType,
    /// Model parameters and weights
    pub parameters: HashMap<String, f64>,
    /// Training configuration
    pub training_config: ModelTrainingConfig,
    /// Model performance metrics
    pub performance: ModelPerformance,
    /// Feature importance scores
    pub feature_importance: HashMap<String, f64>,
    /// Training history and convergence
    pub training_history: TrainingHistory,
    /// Model validation results
    pub validation_results: ValidationResults,
    /// Hyperparameter optimization history
    pub hyperparameter_history: Vec<HyperparameterSnapshot>,
    /// Model interpretability data
    pub interpretability: ModelInterpretability,
}

/// Types of prediction models supported
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum PredictionModelType {
    /// ARIMA time series model
    TimeSeriesARIMA,
    /// LSTM neural network
    TimeSeriesLSTM,
    /// GRU neural network
    TimeSeriesGRU,
    /// Transformer model
    TimeSeriesTransformer,
    /// Linear regression
    RegressionLinear,
    /// Polynomial regression
    RegressionPolynomial,
    /// Random Forest
    RegressionRandomForest,
    /// Support Vector Regression
    RegressionSVR,
    /// XGBoost regression
    RegressionXGBoost,
    /// LightGBM regression
    RegressionLightGBM,
    /// Gaussian Process
    RegressionGaussianProcess,
    /// Ensemble model combining multiple approaches
    EnsembleModel,
    /// Neural network ensemble
    EnsembleNeural,
    /// Bayesian ensemble
    EnsembleBayesian,
    /// Custom user-defined model
    Custom,
}

/// ML prediction result with confidence intervals
#[derive(Debug, Clone)]
pub struct Prediction {
    /// Prediction timestamp
    pub timestamp: Instant,
    /// Predicted values by metric name
    pub values: HashMap<String, f64>,
    /// Confidence intervals for predictions
    pub confidence_intervals: HashMap<String, (f64, f64)>,
    /// Prediction uncertainty quantification
    pub uncertainty: HashMap<String, f64>,
    /// Contributing factors and feature importance
    pub contributing_factors: HashMap<String, f64>,
    /// Model used for prediction
    pub model_id: String,
    /// Prediction horizon
    pub horizon: Duration,
    /// Quality score of prediction
    pub quality_score: f32,
    /// Anomaly detection flags
    pub anomaly_flags: Vec<AnomalyFlag>,
    /// Prediction metadata
    pub metadata: PredictionMetadata,
}

/// Trend prediction with statistical analysis
#[derive(Debug, Clone)]
pub struct TrendPrediction {
    /// Predicted trend direction
    pub direction: TrendDirection,
    /// Prediction confidence (0.0 to 1.0)
    pub confidence: f32,
    /// Time horizon for prediction
    pub horizon: Duration,
    /// Expected magnitude of change
    pub expected_magnitude: f64,
    /// Trend persistence probability
    pub persistence_probability: f32,
    /// Seasonal components if detected
    pub seasonal_components: Vec<SeasonalComponent>,
    /// Trend reversal probability
    pub reversal_probability: f32,
    /// Volatility prediction
    pub volatility_prediction: f32,
    /// Breakpoint probabilities
    pub breakpoint_probabilities: Vec<(Instant, f32)>,
    /// Trend quality assessment
    pub quality_assessment: TrendQualityAssessment,
}

/// Trend directions with statistical significance
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TrendDirection {
    /// Strong upward trend
    StrongIncreasing,
    /// Moderate upward trend
    ModerateIncreasing,
    /// Weak upward trend
    WeakIncreasing,
    /// Statistically stable
    Stable,
    /// Weak downward trend
    WeakDecreasing,
    /// Moderate downward trend
    ModerateDecreasing,
    /// Strong downward trend
    StrongDecreasing,
    /// Cyclical pattern detected
    Cyclical,
    /// High volatility, unclear trend
    Volatile,
    /// Insufficient data
    Indeterminate,
}

/// Historical performance data point
#[derive(Debug, Clone)]
pub struct HistoricalPerformance {
    /// Timestamp of measurement
    pub timestamp: Instant,
    /// Performance metrics collected
    pub metrics: HashMap<String, f64>,
    /// System configuration at time of measurement
    pub configuration: HashMap<String, String>,
    /// Environmental factors
    pub environment: EnvironmentalFactors,
    /// Resource utilization data
    pub resource_utilization: ResourceUtilization,
    /// Workload characteristics
    pub workload_characteristics: WorkloadCharacteristics,
    /// Quality score of data point
    pub data_quality: f32,
    /// Associated optimization strategy
    pub strategy_id: String,
    /// Context information
    pub context: HashMap<String, String>,
    /// Data validation flags
    pub validation_flags: Vec<ValidationFlag>,
}

/// Performance feature extractor
#[derive(Debug, Clone)]
pub struct PerformanceFeatureExtractor {
    /// Extractor name
    pub name: String,
    /// Type of feature being extracted
    pub feature_type: PerformanceFeatureType,
    /// Extraction parameters
    pub parameters: HashMap<String, f64>,
    /// Feature importance weight
    pub importance: f32,
    /// Window size for temporal features
    pub window_size: usize,
    /// Feature transformation pipeline
    pub transformations: Vec<FeatureTransformation>,
    /// Normalization configuration
    pub normalization: NormalizationConfig,
    /// Feature selection criteria
    pub selection_criteria: FeatureSelectionCriteria,
    /// Extraction performance metrics
    pub extraction_metrics: ExtractionMetrics,
    /// Feature dependencies
    pub dependencies: Vec<String>,
}

/// Types of performance features
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum PerformanceFeatureType {
    /// Statistical moments and distributions
    Statistical,
    /// Time-based patterns and trends
    Temporal,
    /// Frequency domain analysis
    Spectral,
    /// Algorithmic complexity measures
    Complexity,
    /// Pattern recognition features
    Pattern,
    /// Contextual and environmental features
    Contextual,
    /// Cross-correlation features
    CrossCorrelation,
    /// Wavelet transform features
    Wavelet,
    /// Fractal dimension features
    Fractal,
    /// Information theory features
    InformationTheory,
    /// Graph-based features
    Graph,
    /// Composite multi-type features
    Composite,
}

/// General feature extractor interface
#[derive(Debug, Clone)]
pub struct FeatureExtractor {
    /// Extractor name
    pub name: String,
    /// Extraction function type
    pub extractor_type: ExtractorType,
    /// Feature importance score
    pub importance: f32,
    /// Extraction parameters
    pub parameters: HashMap<String, f64>,
    /// Computational complexity
    pub complexity: ComputationalComplexity,
    /// Feature stability over time
    pub stability: f32,
    /// Feature correlation with target
    pub target_correlation: f32,
    /// Extraction reliability
    pub reliability: f32,
}

/// Types of feature extractors
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ExtractorType {
    /// Statistical feature extraction
    Statistical,
    /// Temporal pattern extraction
    Temporal,
    /// Frequency domain extraction
    Frequency,
    /// Structural pattern extraction
    Structural,
    /// Contextual feature extraction
    Contextual,
    /// Composite multi-method extraction
    Composite,
    /// Deep learning feature extraction
    DeepLearning,
    /// Kernel-based extraction
    Kernel,
    /// Ensemble extraction methods
    Ensemble,
}

/// Prediction accuracy tracker
#[derive(Debug)]
pub struct PredictionAccuracyTracker {
    /// Accuracy by model
    model_accuracy: HashMap<String, AccuracyMetrics>,
    /// Accuracy history over time
    accuracy_history: VecDeque<AccuracySnapshot>,
    /// Overall system accuracy
    overall_accuracy: f32,
    /// Best performing model identifier
    best_model: Option<String>,
    /// Model ranking by performance
    model_ranking: Vec<(String, f32)>,
    /// Accuracy trends analysis
    accuracy_trends: HashMap<String, TrendAnalysis>,
    /// Cross-validation results
    cv_results: HashMap<String, CrossValidationResults>,
    /// Statistical significance tests
    significance_tests: HashMap<String, SignificanceTestResults>,
    /// Prediction calibration data
    calibration_data: HashMap<String, CalibrationData>,
    /// Accuracy monitoring alerts
    accuracy_alerts: Vec<AccuracyAlert>,
}

/// Accuracy metrics for model evaluation
#[derive(Debug, Clone)]
pub struct AccuracyMetrics {
    /// Mean absolute error
    pub mae: f64,
    /// Root mean square error
    pub rmse: f64,
    /// Mean absolute percentage error
    pub mape: f64,
    /// R-squared coefficient
    pub r_squared: f64,
    /// Mean squared logarithmic error
    pub msle: f64,
    /// Symmetric mean absolute percentage error
    pub smape: f64,
    /// Directional accuracy
    pub directional_accuracy: f32,
    /// Prediction interval coverage
    pub interval_coverage: f32,
    /// Bias measures
    pub bias: f64,
    /// Variance measures
    pub variance: f64,
    /// Model confidence calibration
    pub calibration_error: f64,
    /// Statistical significance
    pub p_value: f64,
    /// Effect size measures
    pub effect_size: f64,
}

/// Accuracy snapshot over time
#[derive(Debug, Clone)]
pub struct AccuracySnapshot {
    /// Snapshot timestamp
    pub timestamp: Instant,
    /// Model accuracies at this time
    pub model_accuracies: HashMap<String, AccuracyMetrics>,
    /// Best model at this time
    pub best_model: String,
    /// Overall system accuracy
    pub system_accuracy: f32,
    /// Data quality score
    pub data_quality: f32,
    /// Environmental conditions
    pub conditions: HashMap<String, String>,
    /// Sample size for accuracy calculation
    pub sample_size: usize,
    /// Confidence intervals
    pub confidence_intervals: HashMap<String, (f64, f64)>,
    /// Model agreement scores
    pub model_agreement: f32,
    /// Anomaly detection results
    pub anomaly_score: f32,
}

/// Predictor configuration
#[derive(Debug, Clone)]
pub struct PredictorConfig {
    /// Prediction horizon
    pub prediction_horizon: Duration,
    /// Model update frequency
    pub update_frequency: Duration,
    /// Minimum training data required
    pub min_training_data: usize,
    /// Maximum historical data to retain
    pub max_historical_data: usize,
    /// Feature extraction configuration
    pub feature_config: FeatureExtractionConfig,
    /// Model ensemble configuration
    pub ensemble_config: EnsembleConfig,
    /// Cross-validation configuration
    pub cv_config: CrossValidationConfig,
    /// Auto-ML configuration
    pub auto_ml_config: AutoMLConfig,
    /// Prediction caching configuration
    pub cache_config: CacheConfig,
    /// Quality thresholds
    pub quality_thresholds: QualityThresholds,
    /// Computational resource limits
    pub resource_limits: ResourceLimits,
    /// Alert configuration
    pub alert_config: AlertConfig,
}

/// Model performance tracking
#[derive(Debug, Clone)]
pub struct ModelPerformance {
    /// Prediction accuracy score
    pub accuracy: f32,
    /// Mean squared error
    pub mse: f64,
    /// Mean absolute error
    pub mae: f64,
    /// Training time
    pub training_time: Duration,
    /// Inference time per prediction
    pub inference_time: Duration,
    /// Memory usage during training
    pub memory_usage: u64,
    /// Model complexity score
    pub complexity: f32,
    /// Generalization capability
    pub generalization: f32,
    /// Robustness to noise
    pub robustness: f32,
    /// Feature sensitivity analysis
    pub feature_sensitivity: HashMap<String, f32>,
    /// Prediction stability
    pub stability: f32,
    /// Model interpretability score
    pub interpretability: f32,
}

/// Online learning configuration
#[derive(Debug, Clone)]
pub struct OnlineLearningConfig {
    /// Enable online learning
    pub enabled: bool,
    /// Learning rate for updates
    pub learning_rate: f32,
    /// Batch size for updates
    pub batch_size: usize,
    /// Update frequency
    pub update_frequency: Duration,
    /// Forgetting factor for old data
    pub forgetting_factor: f32,
    /// Minimum examples before updating
    pub min_examples: usize,
    /// Maximum memory for online learning
    pub max_memory_mb: usize,
    /// Adaptive learning rate
    pub adaptive_lr: bool,
    /// Learning rate decay
    pub lr_decay: f32,
    /// Regularization strength
    pub regularization: f32,
    /// Early stopping configuration
    pub early_stopping: EarlyStoppingConfig,
}

/// Model ensemble for combining predictions
#[derive(Debug)]
pub struct ModelEnsemble {
    /// Component models
    models: Vec<String>,
    /// Model weights for ensemble
    weights: HashMap<String, f32>,
    /// Ensemble method
    ensemble_method: EnsembleMethod,
    /// Diversity measures
    diversity_metrics: DiversityMetrics,
    /// Ensemble performance
    performance: EnsemblePerformance,
    /// Dynamic weight adjustment
    weight_adaptation: WeightAdaptation,
    /// Consensus analysis
    consensus_analyzer: ConsensusAnalyzer,
    /// Ensemble pruning strategy
    pruning_strategy: EnsemblePruningStrategy,
}

/// Ensemble combination methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum EnsembleMethod {
    /// Simple average of predictions
    Average,
    /// Weighted average based on performance
    WeightedAverage,
    /// Median of predictions
    Median,
    /// Best model selection
    BestModel,
    /// Bayesian model averaging
    BayesianAveraging,
    /// Stacking ensemble
    Stacking,
    /// Boosting ensemble
    Boosting,
    /// Bagging ensemble
    Bagging,
    /// Dynamic selection
    DynamicSelection,
    /// Hierarchical ensemble
    Hierarchical,
}

/// Real-time feature processing
#[derive(Debug)]
pub struct FeatureProcessor {
    /// Preprocessing pipeline
    pipeline: Vec<PreprocessingStep>,
    /// Feature scaling parameters
    scaling_params: HashMap<String, ScalingParameters>,
    /// Feature selection mask
    selection_mask: Vec<bool>,
    /// Processing statistics
    processing_stats: ProcessingStatistics,
    /// Feature quality monitors
    quality_monitors: Vec<FeatureQualityMonitor>,
    /// Real-time feature cache
    feature_cache: Arc<RwLock<HashMap<String, CachedFeature>>>,
    /// Streaming feature computation
    streaming_processor: StreamingFeatureProcessor,
    /// Feature drift detection
    drift_detector: FeatureDriftDetector,
}

/// Cached prediction for performance
#[derive(Debug, Clone)]
pub struct CachedPrediction {
    /// Original prediction
    pub prediction: Prediction,
    /// Cache timestamp
    pub cached_at: Instant,
    /// Cache expiry time
    pub expires_at: Instant,
    /// Cache hit count
    pub hit_count: u64,
    /// Cache validity score
    pub validity_score: f32,
    /// Cache metadata
    pub metadata: HashMap<String, String>,
}

/// Cross-validation manager
#[derive(Debug)]
pub struct CrossValidationManager {
    /// Validation strategy
    strategy: CrossValidationStrategy,
    /// Fold configuration
    fold_config: FoldConfiguration,
    /// Validation results
    results: HashMap<String, CrossValidationResults>,
    /// Validation performance tracking
    performance_tracker: ValidationPerformanceTracker,
    /// Statistical testing framework
    statistical_tests: StatisticalTestFramework,
    /// Validation quality assurance
    quality_assurance: ValidationQualityAssurance,
}

/// Cross-validation strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CrossValidationStrategy {
    /// K-fold cross-validation
    KFold,
    /// Stratified K-fold
    StratifiedKFold,
    /// Time series split
    TimeSeriesSplit,
    /// Leave-one-out
    LeaveOneOut,
    /// Monte Carlo cross-validation
    MonteCarlo,
    /// Nested cross-validation
    Nested,
    /// Walk-forward validation
    WalkForward,
    /// Blocked time series validation
    BlockedTimeSeries,
    /// Purged cross-validation
    Purged,
    /// Combinatorial purged cross-validation
    CombinatorialPurged,
}

/// Anomaly detection for prediction quality
#[derive(Debug)]
pub struct PredictionAnomalyDetector {
    /// Anomaly detection algorithms
    detectors: HashMap<String, AnomalyDetectionAlgorithm>,
    /// Anomaly thresholds
    thresholds: AnomalyThresholds,
    /// Detection results history
    detection_history: VecDeque<AnomalyDetectionResult>,
    /// Alert system for anomalies
    alert_system: AnomalyAlertSystem,
    /// Anomaly explanation system
    explanation_system: AnomalyExplanationSystem,
    /// Adaptive threshold adjustment
    adaptive_thresholds: AdaptiveThresholdSystem,
}

/// Auto-ML pipeline for model optimization
#[derive(Debug)]
pub struct AutoMLPipeline {
    /// Available algorithms for search
    algorithm_space: Vec<PredictionModelType>,
    /// Hyperparameter search space
    hyperparameter_space: HashMap<String, ParameterRange>,
    /// Optimization strategy
    optimization_strategy: OptimizationStrategy,
    /// Search history
    search_history: Vec<SearchIteration>,
    /// Best configurations found
    best_configurations: HashMap<String, ModelConfiguration>,
    /// Neural architecture search
    nas_system: NeuralArchitectureSearch,
    /// Meta-learning system
    meta_learning: MetaLearningSystem,
    /// Budget management
    budget_manager: AutoMLBudgetManager,
}

// Additional supporting structures

/// Environmental factors affecting performance
#[derive(Debug, Clone)]
pub struct EnvironmentalFactors {
    /// System load at measurement time
    pub system_load: f32,
    /// Memory pressure
    pub memory_pressure: f32,
    /// GPU utilization
    pub gpu_utilization: f32,
    /// Network conditions
    pub network_conditions: NetworkConditions,
    /// Temperature conditions
    pub temperature: f32,
    /// Power state
    pub power_state: PowerState,
    /// Background processes impact
    pub background_impact: f32,
}

/// Resource utilization data
#[derive(Debug, Clone)]
pub struct ResourceUtilization {
    /// CPU utilization percentage
    pub cpu_utilization: f32,
    /// Memory utilization
    pub memory_utilization: MemoryUtilization,
    /// GPU utilization details
    pub gpu_utilization: GpuUtilization,
    /// I/O utilization
    pub io_utilization: IoUtilization,
    /// Network utilization
    pub network_utilization: NetworkUtilization,
    /// Storage utilization
    pub storage_utilization: StorageUtilization,
}

/// Workload characteristics
#[derive(Debug, Clone)]
pub struct WorkloadCharacteristics {
    /// Data size being processed
    pub data_size: u64,
    /// Operation complexity
    pub operation_complexity: f32,
    /// Parallelization degree
    pub parallelization: f32,
    /// Memory access patterns
    pub memory_patterns: MemoryAccessPatterns,
    /// Computational intensity
    pub compute_intensity: f32,
    /// Data access locality
    pub locality_score: f32,
    /// Workload type classification
    pub workload_type: WorkloadType,
}

impl PerformancePredictor {
    /// Create a new performance predictor
    pub fn new(config: PredictorConfig) -> Self {
        Self {
            models: HashMap::new(),
            historical_data: VecDeque::new(),
            feature_extractors: Self::initialize_feature_extractors(&config),
            accuracy_tracker: PredictionAccuracyTracker::new(),
            config: config.clone(),
            ensemble: ModelEnsemble::new(config.ensemble_config.clone()),
            feature_processor: FeatureProcessor::new(),
            prediction_cache: Arc::new(RwLock::new(HashMap::new())),
            cross_validator: CrossValidationManager::new(config.cv_config.clone()),
            anomaly_detector: PredictionAnomalyDetector::new(),
            auto_ml: AutoMLPipeline::new(config.auto_ml_config.clone()),
        }
    }

    /// Add historical performance data
    pub fn add_historical_data(
        &mut self,
        data: HistoricalPerformance,
    ) -> Result<(), PredictionError> {
        // Validate data quality
        if self.validate_data_quality(&data) {
            self.historical_data.push_back(data);

            // Limit historical data size
            if self.historical_data.len() > self.config.max_historical_data {
                self.historical_data.pop_front();
            }

            // Update models if enough new data
            if self.historical_data.len() % 100 == 0 {
                self.update_models()?;
            }

            Ok(())
        } else {
            Err(PredictionError::InvalidDataQuality)
        }
    }

    /// Train prediction models
    pub fn train_models(&mut self) -> Result<(), PredictionError> {
        if self.historical_data.len() < self.config.min_training_data {
            return Err(PredictionError::InsufficientData);
        }

        // Extract features for training
        let features = self.extract_training_features()?;
        let targets = self.extract_target_values()?;

        // Train each model type
        for model_type in &[
            PredictionModelType::TimeSeriesLSTM,
            PredictionModelType::RegressionRandomForest,
            PredictionModelType::RegressionXGBoost,
            PredictionModelType::EnsembleModel,
        ] {
            let model = self.train_single_model(*model_type, &features, &targets)?;
            self.models.insert(model.id.clone(), model);
        }

        // Update ensemble weights
        self.ensemble.update_weights(&self.models)?;

        // Cross-validate models
        self.cross_validate_models()?;

        Ok(())
    }

    /// Generate prediction for given strategy
    pub fn predict(
        &mut self,
        strategy_id: &str,
        horizon: Duration,
    ) -> Result<Prediction, PredictionError> {
        // Check cache first
        if let Some(cached) = self.get_cached_prediction(strategy_id, horizon) {
            return Ok(cached.prediction);
        }

        // Extract current features
        let features = self.extract_current_features(strategy_id)?;

        // Get ensemble prediction
        let prediction = self.ensemble.predict(&features, horizon)?;

        // Detect anomalies in prediction
        let anomaly_flags = self
            .anomaly_detector
            .detect_prediction_anomalies(&prediction)?;

        // Create final prediction with metadata
        let final_prediction = Prediction {
            timestamp: Instant::now(),
            values: prediction.values,
            confidence_intervals: prediction.confidence_intervals,
            uncertainty: prediction.uncertainty,
            contributing_factors: self.analyze_contributing_factors(&features)?,
            model_id: prediction.model_id,
            horizon,
            quality_score: self.assess_prediction_quality(&prediction)?,
            anomaly_flags,
            metadata: self.create_prediction_metadata(strategy_id, &features)?,
        };

        // Cache the prediction
        self.cache_prediction(strategy_id, &final_prediction)?;

        Ok(final_prediction)
    }

    /// Predict trend for performance metric
    pub fn predict_trend(
        &self,
        metric: &str,
        horizon: Duration,
    ) -> Result<TrendPrediction, PredictionError> {
        let trend_analyzer = TrendAnalyzer::new(&self.historical_data);
        let current_trend = trend_analyzer.analyze_current_trend(metric)?;

        let trend_prediction = TrendPrediction {
            direction: current_trend.direction,
            confidence: current_trend.confidence,
            horizon,
            expected_magnitude: current_trend.expected_magnitude,
            persistence_probability: current_trend.persistence_probability,
            seasonal_components: trend_analyzer.detect_seasonality(metric)?,
            reversal_probability: trend_analyzer.calculate_reversal_probability(metric)?,
            volatility_prediction: trend_analyzer.predict_volatility(metric, horizon)?,
            breakpoint_probabilities: trend_analyzer.detect_breakpoints(metric)?,
            quality_assessment: trend_analyzer.assess_trend_quality(metric)?,
        };

        Ok(trend_prediction)
    }

    /// Update models with new data (online learning)
    pub fn update_models(&mut self) -> Result<(), PredictionError> {
        if !self.config.feature_config.online_learning.enabled {
            return Ok(());
        }

        let recent_data: Vec<_> = self
            .historical_data
            .iter()
            .rev()
            .take(self.config.feature_config.online_learning.batch_size)
            .collect();

        if recent_data.len() < self.config.feature_config.online_learning.min_examples {
            return Ok(());
        }

        // Update each model with recent data
        for model in self.models.values_mut() {
            self.update_model_online(model, &recent_data)?;
        }

        // Update ensemble weights
        self.ensemble.adapt_weights(&self.models)?;

        Ok(())
    }

    /// Get model accuracy metrics
    pub fn get_accuracy_metrics(&self) -> HashMap<String, AccuracyMetrics> {
        self.accuracy_tracker.get_current_metrics()
    }

    /// Get best performing model
    pub fn get_best_model(&self) -> Option<&str> {
        self.accuracy_tracker.get_best_model()
    }

    /// Validate prediction accuracy against actual results
    pub fn validate_prediction(
        &mut self,
        prediction: &Prediction,
        actual: &HashMap<String, f64>,
    ) -> Result<(), PredictionError> {
        let accuracy = self.calculate_prediction_accuracy(prediction, actual)?;
        self.accuracy_tracker
            .add_accuracy_measurement(prediction.model_id.clone(), accuracy);

        // Update model performance metrics
        if let Some(model) = self.models.get_mut(&prediction.model_id) {
            self.update_model_performance(model, &accuracy);
        }

        // Trigger retraining if accuracy drops significantly
        if accuracy.mae > 0.1 && accuracy.r_squared < 0.5 {
            self.schedule_model_retraining()?;
        }

        Ok(())
    }

    /// Export prediction models for external use
    pub fn export_models(&self) -> Result<Vec<ExportedModel>, PredictionError> {
        self.models
            .values()
            .map(|model| self.export_single_model(model))
            .collect()
    }

    /// Import prediction models
    pub fn import_models(&mut self, models: Vec<ExportedModel>) -> Result<(), PredictionError> {
        for exported_model in models {
            let model = self.import_single_model(exported_model)?;
            self.models.insert(model.id.clone(), model);
        }
        Ok(())
    }

    // Private helper methods

    fn initialize_feature_extractors(config: &PredictorConfig) -> Vec<PerformanceFeatureExtractor> {
        vec![
            PerformanceFeatureExtractor::new("statistical", PerformanceFeatureType::Statistical),
            PerformanceFeatureExtractor::new("temporal", PerformanceFeatureType::Temporal),
            PerformanceFeatureExtractor::new("spectral", PerformanceFeatureType::Spectral),
            PerformanceFeatureExtractor::new("complexity", PerformanceFeatureType::Complexity),
            PerformanceFeatureExtractor::new("pattern", PerformanceFeatureType::Pattern),
            PerformanceFeatureExtractor::new("contextual", PerformanceFeatureType::Contextual),
        ]
    }

    fn validate_data_quality(&self, data: &HistoricalPerformance) -> bool {
        data.data_quality >= 0.7
            && !data.metrics.is_empty()
            && data.validation_flags.iter().all(|flag| !flag.is_critical())
    }

    fn extract_training_features(&self) -> Result<Array2<f64>, PredictionError> {
        let mut features = Vec::new();

        for data_point in &self.historical_data {
            let mut feature_vector = Vec::new();

            for extractor in &self.feature_extractors {
                let extracted_features = extractor.extract_features(data_point)?;
                feature_vector.extend(extracted_features);
            }

            features.push(feature_vector);
        }

        if features.is_empty() {
            return Err(PredictionError::NoFeatures);
        }

        let feature_matrix = Array2::from_shape_vec(
            (features.len(), features[0].len()),
            features.into_iter().flatten().collect(),
        )
        .map_err(|_| PredictionError::InvalidFeatureShape)?;

        Ok(feature_matrix)
    }

    fn extract_target_values(&self) -> Result<Array1<f64>, PredictionError> {
        let targets: Vec<f64> = self
            .historical_data
            .iter()
            .filter_map(|data| data.metrics.get("performance_score").copied())
            .collect();

        if targets.is_empty() {
            return Err(PredictionError::NoTargets);
        }

        Ok(Array1::from_vec(targets))
    }

    fn train_single_model(
        &self,
        model_type: PredictionModelType,
        features: &Array2<f64>,
        targets: &Array1<f64>,
    ) -> Result<PredictionModel, PredictionError> {
        let mut model = PredictionModel::new(model_type);

        // Training implementation would depend on model type
        match model_type {
            PredictionModelType::TimeSeriesLSTM => {
                self.train_lstm_model(&mut model, features, targets)?;
            }
            PredictionModelType::RegressionRandomForest => {
                self.train_rf_model(&mut model, features, targets)?;
            }
            PredictionModelType::RegressionXGBoost => {
                self.train_xgboost_model(&mut model, features, targets)?;
            }
            PredictionModelType::EnsembleModel => {
                self.train_ensemble_model(&mut model, features, targets)?;
            }
            _ => return Err(PredictionError::UnsupportedModelType),
        }

        // Validate trained model
        let validation_results = self.validate_trained_model(&model, features, targets)?;
        model.validation_results = validation_results;

        Ok(model)
    }

    fn cross_validate_models(&mut self) -> Result<(), PredictionError> {
        let features = self.extract_training_features()?;
        let targets = self.extract_target_values()?;

        for (model_id, model) in &self.models {
            let cv_results = self
                .cross_validator
                .validate_model(model, &features, &targets)?;
            self.accuracy_tracker
                .add_cv_results(model_id.clone(), cv_results);
        }

        Ok(())
    }

    fn get_cached_prediction(
        &self,
        strategy_id: &str,
        horizon: Duration,
    ) -> Option<&CachedPrediction> {
        let cache = self.prediction_cache.read().expect("lock should not be poisoned");
        let cache_key = format!("{}_{:?}", strategy_id, horizon);

        if let Some(cached) = cache.get(&cache_key) {
            if cached.expires_at > Instant::now() {
                return Some(cached);
            }
        }

        None
    }

    fn extract_current_features(&self, strategy_id: &str) -> Result<Array1<f64>, PredictionError> {
        let latest_data = self
            .historical_data
            .iter()
            .rev()
            .find(|data| data.strategy_id == strategy_id)
            .ok_or(PredictionError::NoDataForStrategy)?;

        let mut feature_vector = Vec::new();

        for extractor in &self.feature_extractors {
            let extracted_features = extractor.extract_features(latest_data)?;
            feature_vector.extend(extracted_features);
        }

        Ok(Array1::from_vec(feature_vector))
    }

    fn analyze_contributing_factors(
        &self,
        features: &Array1<f64>,
    ) -> Result<HashMap<String, f64>, PredictionError> {
        let mut factors = HashMap::new();

        // Analyze feature importance using the best model
        if let Some(best_model_id) = self.accuracy_tracker.get_best_model() {
            if let Some(best_model) = self.models.get(best_model_id) {
                for (i, feature_name) in self.get_feature_names().iter().enumerate() {
                    if let Some(importance) = best_model.feature_importance.get(feature_name) {
                        factors.insert(feature_name.clone(), features[i] * importance);
                    }
                }
            }
        }

        Ok(factors)
    }

    fn assess_prediction_quality(&self, prediction: &Prediction) -> Result<f32, PredictionError> {
        let mut quality_score = 1.0;

        // Reduce quality based on uncertainty
        let avg_uncertainty: f64 =
            prediction.uncertainty.values().sum::<f64>() / prediction.uncertainty.len() as f64;
        quality_score *= (1.0 - avg_uncertainty as f32).max(0.0);

        // Consider model performance
        if let Some(model) = self.models.get(&prediction.model_id) {
            quality_score *= model.performance.accuracy;
        }

        // Consider data recency
        let data_age = self
            .historical_data
            .back()
            .map(|data| data.timestamp.elapsed().as_secs() as f32 / 3600.0)
            .unwrap_or(24.0);
        let recency_factor = (1.0 - (data_age / 24.0).min(1.0)).max(0.1);
        quality_score *= recency_factor;

        Ok(quality_score.clamp(0.0, 1.0))
    }

    fn create_prediction_metadata(
        &self,
        strategy_id: &str,
        features: &Array1<f64>,
    ) -> Result<PredictionMetadata, PredictionError> {
        Ok(PredictionMetadata {
            strategy_id: strategy_id.to_string(),
            feature_count: features.len(),
            data_points_used: self.historical_data.len(),
            model_count: self.models.len(),
            prediction_timestamp: Instant::now(),
            feature_importance_summary: self.summarize_feature_importance()?,
            model_consensus: self.calculate_model_consensus()?,
            data_quality_score: self.calculate_data_quality_score()?,
        })
    }

    fn cache_prediction(
        &mut self,
        strategy_id: &str,
        prediction: &Prediction,
    ) -> Result<(), PredictionError> {
        let cache_key = format!("{}_{:?}", strategy_id, prediction.horizon);
        let cached_prediction = CachedPrediction {
            prediction: prediction.clone(),
            cached_at: Instant::now(),
            expires_at: Instant::now() + Duration::from_secs(300), // 5 minute expiry
            hit_count: 0,
            validity_score: prediction.quality_score,
            metadata: HashMap::new(),
        };

        let mut cache = self.prediction_cache.write().expect("lock should not be poisoned");
        cache.insert(cache_key, cached_prediction);

        Ok(())
    }

    fn get_feature_names(&self) -> Vec<String> {
        self.feature_extractors
            .iter()
            .map(|extractor| extractor.name.clone())
            .collect()
    }

    // Placeholder implementations for complex methods
    fn train_lstm_model(
        &self,
        model: &mut PredictionModel,
        features: &Array2<f64>,
        targets: &Array1<f64>,
    ) -> Result<(), PredictionError> {
        // LSTM training implementation would go here
        Ok(())
    }

    fn train_rf_model(
        &self,
        model: &mut PredictionModel,
        features: &Array2<f64>,
        targets: &Array1<f64>,
    ) -> Result<(), PredictionError> {
        // Random Forest training implementation would go here
        Ok(())
    }

    fn train_xgboost_model(
        &self,
        model: &mut PredictionModel,
        features: &Array2<f64>,
        targets: &Array1<f64>,
    ) -> Result<(), PredictionError> {
        // XGBoost training implementation would go here
        Ok(())
    }

    fn train_ensemble_model(
        &self,
        model: &mut PredictionModel,
        features: &Array2<f64>,
        targets: &Array1<f64>,
    ) -> Result<(), PredictionError> {
        // Ensemble model training implementation would go here
        Ok(())
    }

    fn validate_trained_model(
        &self,
        model: &PredictionModel,
        features: &Array2<f64>,
        targets: &Array1<f64>,
    ) -> Result<ValidationResults, PredictionError> {
        // Model validation implementation
        Ok(ValidationResults::default())
    }

    fn update_model_online(
        &self,
        model: &mut PredictionModel,
        recent_data: &[&HistoricalPerformance],
    ) -> Result<(), PredictionError> {
        // Online learning update implementation
        Ok(())
    }

    fn calculate_prediction_accuracy(
        &self,
        prediction: &Prediction,
        actual: &HashMap<String, f64>,
    ) -> Result<AccuracyMetrics, PredictionError> {
        // Accuracy calculation implementation
        Ok(AccuracyMetrics::default())
    }

    fn update_model_performance(&self, model: &mut PredictionModel, accuracy: &AccuracyMetrics) {
        // Update model performance metrics
        model.performance.accuracy = (1.0 - accuracy.mape as f32).max(0.0);
    }

    fn schedule_model_retraining(&mut self) -> Result<(), PredictionError> {
        // Schedule retraining implementation
        Ok(())
    }

    fn export_single_model(
        &self,
        model: &PredictionModel,
    ) -> Result<ExportedModel, PredictionError> {
        // Model export implementation
        Ok(ExportedModel::default())
    }

    fn import_single_model(
        &self,
        exported: ExportedModel,
    ) -> Result<PredictionModel, PredictionError> {
        // Model import implementation
        Ok(PredictionModel::default())
    }

    fn summarize_feature_importance(&self) -> Result<HashMap<String, f32>, PredictionError> {
        // Feature importance summary
        Ok(HashMap::new())
    }

    fn calculate_model_consensus(&self) -> Result<f32, PredictionError> {
        // Model consensus calculation
        Ok(0.5)
    }

    fn calculate_data_quality_score(&self) -> Result<f32, PredictionError> {
        // Data quality score calculation
        let avg_quality: f32 = self
            .historical_data
            .iter()
            .map(|data| data.data_quality)
            .sum::<f32>()
            / self.historical_data.len() as f32;
        Ok(avg_quality)
    }
}

// Default implementations and additional supporting structures would be implemented here
// Due to space constraints, showing the main structure

impl Default for PredictorConfig {
    fn default() -> Self {
        Self {
            prediction_horizon: Duration::from_secs(3600),
            update_frequency: Duration::from_secs(300),
            min_training_data: 1000,
            max_historical_data: 10000,
            feature_config: FeatureExtractionConfig::default(),
            ensemble_config: EnsembleConfig::default(),
            cv_config: CrossValidationConfig::default(),
            auto_ml_config: AutoMLConfig::default(),
            cache_config: CacheConfig::default(),
            quality_thresholds: QualityThresholds::default(),
            resource_limits: ResourceLimits::default(),
            alert_config: AlertConfig::default(),
        }
    }
}

impl Default for OnlineLearningConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            learning_rate: 0.01,
            batch_size: 32,
            update_frequency: Duration::from_secs(60),
            forgetting_factor: 0.9,
            min_examples: 100,
            max_memory_mb: 512,
            adaptive_lr: true,
            lr_decay: 0.95,
            regularization: 0.01,
            early_stopping: EarlyStoppingConfig::default(),
        }
    }
}

/// Prediction errors
#[derive(Debug)]
pub enum PredictionError {
    InvalidDataQuality,
    InsufficientData,
    NoFeatures,
    InvalidFeatureShape,
    NoTargets,
    UnsupportedModelType,
    NoDataForStrategy,
    ModelTrainingFailed,
    CrossValidationFailed,
    CacheError,
    ExportError,
    ImportError,
}

impl std::fmt::Display for PredictionError {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            PredictionError::InvalidDataQuality => {
                write!(f, "Data quality is insufficient for prediction")
            }
            PredictionError::InsufficientData => write!(f, "Not enough training data available"),
            PredictionError::NoFeatures => write!(f, "No features could be extracted"),
            PredictionError::InvalidFeatureShape => write!(f, "Feature matrix has invalid shape"),
            PredictionError::NoTargets => write!(f, "No target values found"),
            PredictionError::UnsupportedModelType => write!(f, "Model type is not supported"),
            PredictionError::NoDataForStrategy => {
                write!(f, "No data available for the specified strategy")
            }
            PredictionError::ModelTrainingFailed => write!(f, "Model training failed"),
            PredictionError::CrossValidationFailed => write!(f, "Cross-validation failed"),
            PredictionError::CacheError => write!(f, "Prediction cache error"),
            PredictionError::ExportError => write!(f, "Model export failed"),
            PredictionError::ImportError => write!(f, "Model import failed"),
        }
    }
}

impl std::error::Error for PredictionError {}

// Placeholder structures for compilation - full implementations would be provided separately
#[derive(Debug, Default)]
pub struct ModelTrainingConfig;

#[derive(Debug, Default)]
pub struct TrainingHistory;

#[derive(Debug, Default)]
pub struct ValidationResults;

#[derive(Debug, Default)]
pub struct HyperparameterSnapshot;

#[derive(Debug, Default)]
pub struct ModelInterpretability;

#[derive(Debug, Default)]
pub struct SeasonalComponent;

#[derive(Debug, Default)]
pub struct TrendQualityAssessment;

#[derive(Debug, Default)]
pub struct ValidationFlag;

#[derive(Debug, Default)]
pub struct NetworkConditions;

#[derive(Debug, Default)]
pub struct PowerState;

#[derive(Debug, Default)]
pub struct MemoryUtilization;

#[derive(Debug, Default)]
pub struct GpuUtilization;

#[derive(Debug, Default)]
pub struct IoUtilization;

#[derive(Debug, Default)]
pub struct NetworkUtilization;

#[derive(Debug, Default)]
pub struct StorageUtilization;

#[derive(Debug, Default)]
pub struct MemoryAccessPatterns;

#[derive(Debug, Default)]
pub struct WorkloadType;

#[derive(Debug, Default)]
pub struct TrendAnalysis;

#[derive(Debug, Default)]
pub struct CrossValidationResults;

#[derive(Debug, Default)]
pub struct SignificanceTestResults;

#[derive(Debug, Default)]
pub struct CalibrationData;

#[derive(Debug, Default)]
pub struct AccuracyAlert;

#[derive(Debug, Default)]
pub struct FeatureExtractionConfig;

#[derive(Debug, Default)]
pub struct EnsembleConfig;

#[derive(Debug, Default)]
pub struct CrossValidationConfig;

#[derive(Debug, Default)]
pub struct AutoMLConfig;

#[derive(Debug, Default)]
pub struct CacheConfig;

#[derive(Debug, Default)]
pub struct QualityThresholds;

#[derive(Debug, Default)]
pub struct ResourceLimits;

#[derive(Debug, Default)]
pub struct AlertConfig;

#[derive(Debug, Default)]
pub struct EarlyStoppingConfig;

#[derive(Debug, Default)]
pub struct DiversityMetrics;

#[derive(Debug, Default)]
pub struct EnsemblePerformance;

#[derive(Debug, Default)]
pub struct WeightAdaptation;

#[derive(Debug, Default)]
pub struct ConsensusAnalyzer;

#[derive(Debug, Default)]
pub struct EnsemblePruningStrategy;

#[derive(Debug, Default)]
pub struct PreprocessingStep;

#[derive(Debug, Default)]
pub struct ScalingParameters;

#[derive(Debug, Default)]
pub struct ProcessingStatistics;

#[derive(Debug, Default)]
pub struct FeatureQualityMonitor;

#[derive(Debug, Default)]
pub struct CachedFeature;

#[derive(Debug, Default)]
pub struct StreamingFeatureProcessor;

#[derive(Debug, Default)]
pub struct FeatureDriftDetector;

#[derive(Debug, Default)]
pub struct FoldConfiguration;

#[derive(Debug, Default)]
pub struct ValidationPerformanceTracker;

#[derive(Debug, Default)]
pub struct StatisticalTestFramework;

#[derive(Debug, Default)]
pub struct ValidationQualityAssurance;

#[derive(Debug, Default)]
pub struct AnomalyDetectionAlgorithm;

#[derive(Debug, Default)]
pub struct AnomalyThresholds;

#[derive(Debug, Default)]
pub struct AnomalyDetectionResult;

#[derive(Debug, Default)]
pub struct AnomalyAlertSystem;

#[derive(Debug, Default)]
pub struct AnomalyExplanationSystem;

#[derive(Debug, Default)]
pub struct AdaptiveThresholdSystem;

#[derive(Debug, Default)]
pub struct ParameterRange;

#[derive(Debug, Default)]
pub struct OptimizationStrategy;

#[derive(Debug, Default)]
pub struct SearchIteration;

#[derive(Debug, Default)]
pub struct ModelConfiguration;

#[derive(Debug, Default)]
pub struct NeuralArchitectureSearch;

#[derive(Debug, Default)]
pub struct MetaLearningSystem;

#[derive(Debug, Default)]
pub struct AutoMLBudgetManager;

#[derive(Debug, Default)]
pub struct ComputationalComplexity;

#[derive(Debug, Default)]
pub struct FeatureTransformation;

#[derive(Debug, Default)]
pub struct NormalizationConfig;

#[derive(Debug, Default)]
pub struct FeatureSelectionCriteria;

#[derive(Debug, Default)]
pub struct ExtractionMetrics;

#[derive(Debug, Default)]
pub struct AnomalyFlag;

#[derive(Debug, Default)]
pub struct PredictionMetadata;

#[derive(Debug, Default)]
pub struct ExportedModel;

#[derive(Debug, Default)]
pub struct TrendAnalyzer;

// Additional implementation stubs
impl ValidationFlag {
    fn is_critical(&self) -> bool {
        false
    }
}

impl AccuracyMetrics {
    fn default() -> Self {
        Self {
            mae: 0.0,
            rmse: 0.0,
            mape: 0.0,
            r_squared: 0.0,
            msle: 0.0,
            smape: 0.0,
            directional_accuracy: 0.0,
            interval_coverage: 0.0,
            bias: 0.0,
            variance: 0.0,
            calibration_error: 0.0,
            p_value: 0.0,
            effect_size: 0.0,
        }
    }
}

impl PredictionModel {
    fn new(model_type: PredictionModelType) -> Self {
        Self {
            id: format!("{:?}_{}", model_type, Instant::now().elapsed().as_nanos()),
            model_type,
            parameters: HashMap::new(),
            training_config: ModelTrainingConfig::default(),
            performance: ModelPerformance::default(),
            feature_importance: HashMap::new(),
            training_history: TrainingHistory::default(),
            validation_results: ValidationResults::default(),
            hyperparameter_history: Vec::new(),
            interpretability: ModelInterpretability::default(),
        }
    }

    fn default() -> Self {
        Self::new(PredictionModelType::RegressionLinear)
    }
}

impl PerformanceFeatureExtractor {
    fn new(name: &str, feature_type: PerformanceFeatureType) -> Self {
        Self {
            name: name.to_string(),
            feature_type,
            parameters: HashMap::new(),
            importance: 1.0,
            window_size: 100,
            transformations: Vec::new(),
            normalization: NormalizationConfig::default(),
            selection_criteria: FeatureSelectionCriteria::default(),
            extraction_metrics: ExtractionMetrics::default(),
            dependencies: Vec::new(),
        }
    }

    fn extract_features(&self, data: &HistoricalPerformance) -> Result<Vec<f64>, PredictionError> {
        // Feature extraction implementation
        Ok(vec![0.0; 10]) // Placeholder
    }
}

impl PredictionAccuracyTracker {
    fn new() -> Self {
        Self {
            model_accuracy: HashMap::new(),
            accuracy_history: VecDeque::new(),
            overall_accuracy: 0.0,
            best_model: None,
            model_ranking: Vec::new(),
            accuracy_trends: HashMap::new(),
            cv_results: HashMap::new(),
            significance_tests: HashMap::new(),
            calibration_data: HashMap::new(),
            accuracy_alerts: Vec::new(),
        }
    }

    fn get_current_metrics(&self) -> HashMap<String, AccuracyMetrics> {
        self.model_accuracy.clone()
    }

    fn get_best_model(&self) -> Option<&str> {
        self.best_model.as_deref()
    }

    fn add_accuracy_measurement(&mut self, model_id: String, accuracy: AccuracyMetrics) {
        self.model_accuracy.insert(model_id, accuracy);
    }

    fn add_cv_results(&mut self, model_id: String, results: CrossValidationResults) {
        self.cv_results.insert(model_id, results);
    }
}

impl ModelEnsemble {
    fn new(config: EnsembleConfig) -> Self {
        Self {
            models: Vec::new(),
            weights: HashMap::new(),
            ensemble_method: EnsembleMethod::WeightedAverage,
            diversity_metrics: DiversityMetrics::default(),
            performance: EnsemblePerformance::default(),
            weight_adaptation: WeightAdaptation::default(),
            consensus_analyzer: ConsensusAnalyzer::default(),
            pruning_strategy: EnsemblePruningStrategy::default(),
        }
    }

    fn update_weights(
        &mut self,
        models: &HashMap<String, PredictionModel>,
    ) -> Result<(), PredictionError> {
        // Weight update implementation
        Ok(())
    }

    fn adapt_weights(
        &mut self,
        models: &HashMap<String, PredictionModel>,
    ) -> Result<(), PredictionError> {
        // Adaptive weight update implementation
        Ok(())
    }

    fn predict(
        &self,
        features: &Array1<f64>,
        horizon: Duration,
    ) -> Result<Prediction, PredictionError> {
        // Ensemble prediction implementation
        Ok(Prediction {
            timestamp: Instant::now(),
            values: HashMap::new(),
            confidence_intervals: HashMap::new(),
            uncertainty: HashMap::new(),
            contributing_factors: HashMap::new(),
            model_id: "ensemble".to_string(),
            horizon,
            quality_score: 0.8,
            anomaly_flags: Vec::new(),
            metadata: PredictionMetadata::default(),
        })
    }
}

impl FeatureProcessor {
    fn new() -> Self {
        Self {
            pipeline: Vec::new(),
            scaling_params: HashMap::new(),
            selection_mask: Vec::new(),
            processing_stats: ProcessingStatistics::default(),
            quality_monitors: Vec::new(),
            feature_cache: Arc::new(RwLock::new(HashMap::new())),
            streaming_processor: StreamingFeatureProcessor::default(),
            drift_detector: FeatureDriftDetector::default(),
        }
    }
}

impl CrossValidationManager {
    fn new(config: CrossValidationConfig) -> Self {
        Self {
            strategy: CrossValidationStrategy::KFold,
            fold_config: FoldConfiguration::default(),
            results: HashMap::new(),
            performance_tracker: ValidationPerformanceTracker::default(),
            statistical_tests: StatisticalTestFramework::default(),
            quality_assurance: ValidationQualityAssurance::default(),
        }
    }

    fn validate_model(
        &self,
        model: &PredictionModel,
        features: &Array2<f64>,
        targets: &Array1<f64>,
    ) -> Result<CrossValidationResults, PredictionError> {
        // Cross-validation implementation
        Ok(CrossValidationResults::default())
    }
}

impl PredictionAnomalyDetector {
    fn new() -> Self {
        Self {
            detectors: HashMap::new(),
            thresholds: AnomalyThresholds::default(),
            detection_history: VecDeque::new(),
            alert_system: AnomalyAlertSystem::default(),
            explanation_system: AnomalyExplanationSystem::default(),
            adaptive_thresholds: AdaptiveThresholdSystem::default(),
        }
    }

    fn detect_prediction_anomalies(
        &self,
        prediction: &Prediction,
    ) -> Result<Vec<AnomalyFlag>, PredictionError> {
        // Anomaly detection implementation
        Ok(Vec::new())
    }
}

impl AutoMLPipeline {
    fn new(config: AutoMLConfig) -> Self {
        Self {
            algorithm_space: Vec::new(),
            hyperparameter_space: HashMap::new(),
            optimization_strategy: OptimizationStrategy::default(),
            search_history: Vec::new(),
            best_configurations: HashMap::new(),
            nas_system: NeuralArchitectureSearch::default(),
            meta_learning: MetaLearningSystem::default(),
            budget_manager: AutoMLBudgetManager::default(),
        }
    }
}

impl TrendAnalyzer {
    fn new(data: &VecDeque<HistoricalPerformance>) -> Self {
        Self::default()
    }

    fn analyze_current_trend(&self, metric: &str) -> Result<TrendAnalysis, PredictionError> {
        Ok(TrendAnalysis::default())
    }

    fn detect_seasonality(&self, metric: &str) -> Result<Vec<SeasonalComponent>, PredictionError> {
        Ok(Vec::new())
    }

    fn calculate_reversal_probability(&self, metric: &str) -> Result<f32, PredictionError> {
        Ok(0.1)
    }

    fn predict_volatility(&self, metric: &str, horizon: Duration) -> Result<f32, PredictionError> {
        Ok(0.1)
    }

    fn detect_breakpoints(&self, metric: &str) -> Result<Vec<(Instant, f32)>, PredictionError> {
        Ok(Vec::new())
    }

    fn assess_trend_quality(
        &self,
        metric: &str,
    ) -> Result<TrendQualityAssessment, PredictionError> {
        Ok(TrendQualityAssessment::default())
    }
}