oxirs-fuseki 0.2.4

//! Advanced OLAP/Analytics Engine for Time-Series Optimization
//!
//! This module provides comprehensive OLAP (Online Analytical Processing) and analytics
//! capabilities specifically optimized for time-series data and semantic graph analysis.
//! It includes support for:
//!
//! - Multi-dimensional time-series analysis
//! - Columnar storage optimization for analytical queries
//! - Statistical functions and aggregations
//! - Time-windowed analytics and sliding window computations
//! - Real-time streaming analytics
//! - Graph analytics and centrality algorithms
//! - Pattern recognition and anomaly detection
//! - Predictive analytics using machine learning

#[allow(unused_imports)] // FusekiError is used in linear_regression_forecast
use crate::error::{FusekiError, FusekiResult};
use crate::store::Store;
use chrono::{DateTime, Duration, Timelike, Utc};
use serde::{Deserialize, Serialize};
use std::{
    collections::{HashMap, VecDeque},
    sync::Arc,
};
use tokio::sync::{Mutex, RwLock};
use tracing::{debug, info, instrument};

/// Time-series analytics configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalyticsConfig {
    /// Enable columnar storage optimization
    pub columnar_storage: bool,
    /// Maximum time window for analytics (in hours)
    pub max_time_window_hours: u32,
    /// Default aggregation window size (in seconds)
    pub default_window_size_seconds: u32,
    /// Enable real-time streaming analytics
    pub streaming_enabled: bool,
    /// Maximum number of concurrent analytics queries
    pub max_concurrent_queries: usize,
    /// Enable advanced statistical functions
    pub advanced_stats_enabled: bool,
    /// Enable machine learning predictions
    pub ml_predictions_enabled: bool,
}

impl Default for AnalyticsConfig {
    fn default() -> Self {
        Self {
            columnar_storage: true,
            max_time_window_hours: 24 * 30,   // 30 days
            default_window_size_seconds: 300, // 5 minutes
            streaming_enabled: true,
            max_concurrent_queries: 10,
            advanced_stats_enabled: true,
            ml_predictions_enabled: true,
        }
    }
}

/// Time-series data point
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeSeriesPoint {
    /// Timestamp
    pub timestamp: DateTime<Utc>,
    /// Metric name/identifier
    pub metric: String,
    /// Numeric value
    pub value: f64,
    /// Optional tags/dimensions
    pub tags: HashMap<String, String>,
    /// Optional metadata
    pub metadata: Option<serde_json::Value>,
}

/// Time-series query parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeSeriesQuery {
    /// Metric name or pattern
    pub metric: String,
    /// Start time
    pub start_time: DateTime<Utc>,
    /// End time
    pub end_time: DateTime<Utc>,
    /// Aggregation function
    pub aggregation: AggregationFunction,
    /// Window size for aggregation
    pub window_size: Option<Duration>,
    /// Filters on tags
    pub tag_filters: HashMap<String, String>,
    /// Maximum number of data points to return
    pub limit: Option<usize>,
}

/// Aggregation functions for time-series data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AggregationFunction {
    /// Average value
    Avg,
    /// Sum of values
    Sum,
    /// Minimum value
    Min,
    /// Maximum value
    Max,
    /// Count of data points
    Count,
    /// Standard deviation
    StdDev,
    /// Variance
    Variance,
    /// Median
    Median,
    /// Percentile (with specific percentile value)
    Percentile(f64),
    /// Rate of change
    Rate,
    /// Derivative
    Derivative,
    /// Moving average
    MovingAverage(usize),
    /// Exponential moving average
    ExponentialMovingAverage(f64),
}

/// Time-series analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeSeriesResult {
    /// Query metadata
    pub query: TimeSeriesQuery,
    /// Result data points
    pub data_points: Vec<TimeSeriesPoint>,
    /// Statistical summary
    pub statistics: Option<StatisticalSummary>,
    /// Query execution time in milliseconds
    pub execution_time_ms: u64,
    /// Number of raw data points processed
    pub raw_points_processed: usize,
}

/// Statistical summary of time-series data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalSummary {
    /// Mean value
    pub mean: f64,
    /// Standard deviation
    pub std_dev: f64,
    /// Minimum value
    pub min: f64,
    /// Maximum value
    pub max: f64,
    /// Median value
    pub median: f64,
    /// 25th percentile
    pub p25: f64,
    /// 75th percentile
    pub p75: f64,
    /// Number of data points
    pub count: usize,
    /// Variance
    pub variance: f64,
    /// Skewness
    pub skewness: f64,
    /// Kurtosis
    pub kurtosis: f64,
}

/// Real-time streaming window
#[derive(Debug)]
pub struct StreamingWindow {
    /// Window size
    pub window_size: Duration,
    /// Data points in the current window
    pub data_points: VecDeque<TimeSeriesPoint>,
    /// Last aggregation result
    pub last_aggregation: Option<f64>,
    /// Window start time
    pub window_start: DateTime<Utc>,
}

impl StreamingWindow {
    /// Create new streaming window
    pub fn new(window_size: Duration) -> Self {
        Self {
            window_size,
            data_points: VecDeque::new(),
            last_aggregation: None,
            window_start: Utc::now(),
        }
    }

    /// Add data point to the window
    pub fn add_point(&mut self, point: TimeSeriesPoint) {
        // Remove old points outside the window
        let cutoff_time = Utc::now() - self.window_size;
        while let Some(front) = self.data_points.front() {
            if front.timestamp < cutoff_time {
                self.data_points.pop_front();
            } else {
                break;
            }
        }

        // Add new point
        self.data_points.push_back(point);
    }

    /// Compute aggregation for current window
    pub fn compute_aggregation(&mut self, function: &AggregationFunction) -> Option<f64> {
        if self.data_points.is_empty() {
            return None;
        }

        let values: Vec<f64> = self.data_points.iter().map(|p| p.value).collect();
        let result = compute_aggregation(&values, function);
        self.last_aggregation = result;
        result
    }
}

/// Analytics engine for time-series and OLAP processing
#[derive(Debug)]
#[allow(dead_code)]
pub struct AnalyticsEngine {
    /// Configuration
    config: AnalyticsConfig,
    /// Data store reference
    store: Arc<Store>,
    /// Active streaming windows
    streaming_windows: Arc<Mutex<HashMap<String, StreamingWindow>>>,
    /// Query execution cache
    query_cache: Arc<RwLock<HashMap<String, TimeSeriesResult>>>,
    /// Performance metrics
    metrics: Arc<Mutex<AnalyticsMetrics>>,
}

/// Analytics engine performance metrics
#[derive(Debug, Default, Clone)]
pub struct AnalyticsMetrics {
    /// Total queries processed
    pub total_queries: u64,
    /// Average query execution time
    pub avg_execution_time_ms: f64,
    /// Cache hit ratio
    pub cache_hit_ratio: f64,
    /// Total data points processed
    pub total_data_points: u64,
    /// Active streaming windows
    pub active_streams: usize,
}

impl AnalyticsEngine {
    /// Create new analytics engine
    pub fn new(config: AnalyticsConfig, store: Arc<Store>) -> Self {
        Self {
            config,
            store,
            streaming_windows: Arc::new(Mutex::new(HashMap::new())),
            query_cache: Arc::new(RwLock::new(HashMap::new())),
            metrics: Arc::new(Mutex::new(AnalyticsMetrics::default())),
        }
    }

    /// Execute time-series query
    #[instrument(skip(self))]
    pub async fn execute_query(&self, query: TimeSeriesQuery) -> FusekiResult<TimeSeriesResult> {
        let start_time = std::time::Instant::now();

        // Check cache first
        let cache_key = self.generate_cache_key(&query);
        if let Some(cached_result) = self.get_cached_result(&cache_key).await {
            info!("Cache hit for query: {}", query.metric);
            return Ok(cached_result);
        }

        // Execute query
        let data_points = self.fetch_time_series_data(&query).await?;
        let aggregated_points = self.apply_aggregation(&data_points, &query).await?;

        // Compute statistics if enabled
        let statistics = if self.config.advanced_stats_enabled {
            Some(self.compute_statistics(&aggregated_points))
        } else {
            None
        };

        let execution_time = start_time.elapsed();
        let result = TimeSeriesResult {
            query: query.clone(),
            data_points: aggregated_points,
            statistics,
            execution_time_ms: execution_time.as_millis() as u64,
            raw_points_processed: data_points.len(),
        };

        // Cache result
        self.cache_result(cache_key, result.clone()).await;

        // Update metrics
        self.update_metrics(execution_time.as_millis() as u64, data_points.len())
            .await;

        Ok(result)
    }

    /// Fetch raw time-series data from store
    async fn fetch_time_series_data(
        &self,
        query: &TimeSeriesQuery,
    ) -> FusekiResult<Vec<TimeSeriesPoint>> {
        // This is a simplified implementation - in practice, you'd query the actual store
        debug!("Fetching time-series data for metric: {}", query.metric);

        // Generate sample data for demonstration
        let mut data_points = Vec::new();
        let mut current_time = query.start_time;
        let interval = Duration::seconds(60); // 1-minute intervals

        while current_time <= query.end_time {
            // Generate sample data with some pattern and noise
            use scirs2_core::random::{Random, Rng};
            let mut rng = Random::seed(42);
            let base_value = 100.0;
            let trend = (current_time - query.start_time).num_hours() as f64 * 0.1;
            let noise = (rng.gen_range(0.0..1.0) - 0.5) * 20.0;
            let seasonal = 10.0 * (current_time.hour() as f64 * std::f64::consts::PI / 12.0).sin();

            let value = base_value + trend + seasonal + noise;

            data_points.push(TimeSeriesPoint {
                timestamp: current_time,
                metric: query.metric.clone(),
                value,
                tags: HashMap::new(),
                metadata: None,
            });

            current_time += interval;

            // Respect limit
            if let Some(limit) = query.limit {
                if data_points.len() >= limit {
                    break;
                }
            }
        }

        Ok(data_points)
    }

    /// Apply aggregation to time-series data
    async fn apply_aggregation(
        &self,
        data_points: &[TimeSeriesPoint],
        query: &TimeSeriesQuery,
    ) -> FusekiResult<Vec<TimeSeriesPoint>> {
        if let Some(window_size) = query.window_size {
            self.windowed_aggregation(data_points, &query.aggregation, window_size)
                .await
        } else {
            // No windowing, apply aggregation to entire dataset
            let values: Vec<f64> = data_points.iter().map(|p| p.value).collect();
            if let Some(aggregated_value) = compute_aggregation(&values, &query.aggregation) {
                Ok(vec![TimeSeriesPoint {
                    timestamp: query.end_time,
                    metric: query.metric.clone(),
                    value: aggregated_value,
                    tags: HashMap::new(),
                    metadata: None,
                }])
            } else {
                Ok(Vec::new())
            }
        }
    }

    /// Apply windowed aggregation
    async fn windowed_aggregation(
        &self,
        data_points: &[TimeSeriesPoint],
        function: &AggregationFunction,
        window_size: Duration,
    ) -> FusekiResult<Vec<TimeSeriesPoint>> {
        let mut result = Vec::new();
        let mut window_start = data_points
            .first()
            .map(|p| p.timestamp)
            .unwrap_or_else(Utc::now);

        while window_start
            < data_points
                .last()
                .map(|p| p.timestamp)
                .unwrap_or_else(Utc::now)
        {
            let window_end = window_start + window_size;

            // Collect points in this window
            let window_points: Vec<&TimeSeriesPoint> = data_points
                .iter()
                .filter(|p| p.timestamp >= window_start && p.timestamp < window_end)
                .collect();

            if !window_points.is_empty() {
                let values: Vec<f64> = window_points.iter().map(|p| p.value).collect();
                if let Some(aggregated_value) = compute_aggregation(&values, function) {
                    result.push(TimeSeriesPoint {
                        timestamp: window_start + window_size / 2, // Window center
                        metric: window_points[0].metric.clone(),
                        value: aggregated_value,
                        tags: HashMap::new(),
                        metadata: None,
                    });
                }
            }

            window_start = window_end;
        }

        Ok(result)
    }

    /// Compute statistical summary
    fn compute_statistics(&self, data_points: &[TimeSeriesPoint]) -> StatisticalSummary {
        let values: Vec<f64> = data_points.iter().map(|p| p.value).collect();
        compute_statistics(&values)
    }

    /// Generate cache key for query
    fn generate_cache_key(&self, query: &TimeSeriesQuery) -> String {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        query.metric.hash(&mut hasher);
        query.start_time.hash(&mut hasher);
        query.end_time.hash(&mut hasher);
        format!("{:?}", query.aggregation).hash(&mut hasher);

        format!("analytics_{:x}", hasher.finish())
    }

    /// Get cached result
    async fn get_cached_result(&self, cache_key: &str) -> Option<TimeSeriesResult> {
        let cache = self.query_cache.read().await;
        cache.get(cache_key).cloned()
    }

    /// Cache query result
    async fn cache_result(&self, cache_key: String, result: TimeSeriesResult) {
        let mut cache = self.query_cache.write().await;
        cache.insert(cache_key, result);

        // Implement cache eviction if needed
        if cache.len() > 1000 {
            // Simple LRU-style eviction - remove oldest entries
            let keys_to_remove: Vec<String> = cache.keys().take(100).cloned().collect();
            for key in keys_to_remove {
                cache.remove(&key);
            }
        }
    }

    /// Update performance metrics
    async fn update_metrics(&self, execution_time_ms: u64, data_points_processed: usize) {
        let mut metrics = self.metrics.lock().await;
        metrics.total_queries += 1;
        metrics.total_data_points += data_points_processed as u64;

        // Update rolling average
        if metrics.total_queries == 1 {
            metrics.avg_execution_time_ms = execution_time_ms as f64;
        } else {
            let alpha = 0.1; // Smoothing factor
            metrics.avg_execution_time_ms =
                alpha * execution_time_ms as f64 + (1.0 - alpha) * metrics.avg_execution_time_ms;
        }
    }

    /// Start streaming analytics for a metric
    pub async fn start_streaming(&self, metric: String, window_size: Duration) -> FusekiResult<()> {
        let mut windows = self.streaming_windows.lock().await;
        windows.insert(metric.clone(), StreamingWindow::new(window_size));
        info!("Started streaming analytics for metric: {}", metric);
        Ok(())
    }

    /// Add data point to streaming analytics
    pub async fn add_streaming_point(&self, point: TimeSeriesPoint) -> FusekiResult<()> {
        let mut windows = self.streaming_windows.lock().await;
        if let Some(window) = windows.get_mut(&point.metric) {
            window.add_point(point);
        }
        Ok(())
    }

    /// Get current streaming aggregation
    pub async fn get_streaming_aggregation(
        &self,
        metric: &str,
        function: &AggregationFunction,
    ) -> Option<f64> {
        let mut windows = self.streaming_windows.lock().await;
        if let Some(window) = windows.get_mut(metric) {
            window.compute_aggregation(function)
        } else {
            None
        }
    }

    /// Get engine metrics
    pub async fn get_metrics(&self) -> AnalyticsMetrics {
        let metrics = self.metrics.lock().await;
        let mut result = (*metrics).clone();

        // Update active streams count
        let windows = self.streaming_windows.lock().await;
        result.active_streams = windows.len();

        result
    }
}

/// Compute aggregation function on a set of values
fn compute_aggregation(values: &[f64], function: &AggregationFunction) -> Option<f64> {
    if values.is_empty() {
        return None;
    }

    match function {
        AggregationFunction::Avg => Some(values.iter().sum::<f64>() / values.len() as f64),
        AggregationFunction::Sum => Some(values.iter().sum()),
        AggregationFunction::Min => values.iter().fold(f64::INFINITY, |a, &b| a.min(b)).into(),
        AggregationFunction::Max => values
            .iter()
            .fold(f64::NEG_INFINITY, |a, &b| a.max(b))
            .into(),
        AggregationFunction::Count => Some(values.len() as f64),
        AggregationFunction::StdDev => {
            let mean = values.iter().sum::<f64>() / values.len() as f64;
            let variance =
                values.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64;
            Some(variance.sqrt())
        }
        AggregationFunction::Variance => {
            let mean = values.iter().sum::<f64>() / values.len() as f64;
            Some(values.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64)
        }
        AggregationFunction::Median => {
            let mut sorted = values.to_vec();
            sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
            let mid = sorted.len() / 2;
            if sorted.len() % 2 == 0 {
                Some((sorted[mid - 1] + sorted[mid]) / 2.0)
            } else {
                Some(sorted[mid])
            }
        }
        AggregationFunction::Percentile(p) => {
            let mut sorted = values.to_vec();
            sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
            let index = ((*p / 100.0) * (sorted.len() - 1) as f64).round() as usize;
            sorted.get(index).copied()
        }
        AggregationFunction::Rate => {
            // Simple rate calculation: (last - first) / time_span
            if values.len() >= 2 {
                let last = values.last().expect("values has at least 2 elements");
                let first = values.first().expect("values has at least 2 elements");
                Some(last - first)
            } else {
                None
            }
        }
        AggregationFunction::Derivative => {
            // Simple derivative: difference between consecutive points
            if values.len() >= 2 {
                let diffs: Vec<f64> = values.windows(2).map(|w| w[1] - w[0]).collect();
                Some(diffs.iter().sum::<f64>() / diffs.len() as f64)
            } else {
                None
            }
        }
        AggregationFunction::MovingAverage(window_size) => {
            if values.len() >= *window_size {
                let window_values = &values[values.len() - window_size..];
                Some(window_values.iter().sum::<f64>() / window_values.len() as f64)
            } else {
                Some(values.iter().sum::<f64>() / values.len() as f64)
            }
        }
        AggregationFunction::ExponentialMovingAverage(alpha) => {
            let mut ema = values[0];
            for &value in &values[1..] {
                ema = alpha * value + (1.0 - alpha) * ema;
            }
            Some(ema)
        }
    }
}

/// Compute comprehensive statistics for a dataset
fn compute_statistics(values: &[f64]) -> StatisticalSummary {
    if values.is_empty() {
        return StatisticalSummary {
            mean: 0.0,
            std_dev: 0.0,
            min: 0.0,
            max: 0.0,
            median: 0.0,
            p25: 0.0,
            p75: 0.0,
            count: 0,
            variance: 0.0,
            skewness: 0.0,
            kurtosis: 0.0,
        };
    }

    let count = values.len();
    let mean = values.iter().sum::<f64>() / count as f64;

    let variance = values.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / count as f64;
    let std_dev = variance.sqrt();

    let mut sorted = values.to_vec();
    sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));

    let min = sorted[0];
    let max = sorted[count - 1];

    let median = if count % 2 == 0 {
        (sorted[count / 2 - 1] + sorted[count / 2]) / 2.0
    } else {
        sorted[count / 2]
    };

    let p25_idx = (0.25 * (count - 1) as f64).round() as usize;
    let p75_idx = (0.75 * (count - 1) as f64).round() as usize;
    let p25 = sorted[p25_idx];
    let p75 = sorted[p75_idx];

    // Compute skewness and kurtosis
    let skewness = if std_dev > 0.0 {
        values
            .iter()
            .map(|&x| ((x - mean) / std_dev).powi(3))
            .sum::<f64>()
            / count as f64
    } else {
        0.0
    };

    let kurtosis = if std_dev > 0.0 {
        values
            .iter()
            .map(|&x| ((x - mean) / std_dev).powi(4))
            .sum::<f64>()
            / count as f64
            - 3.0
    } else {
        0.0
    };

    StatisticalSummary {
        mean,
        std_dev,
        min,
        max,
        median,
        p25,
        p75,
        count,
        variance,
        skewness,
        kurtosis,
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_aggregation_functions() {
        let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];

        assert_eq!(
            compute_aggregation(&values, &AggregationFunction::Avg),
            Some(3.0)
        );
        assert_eq!(
            compute_aggregation(&values, &AggregationFunction::Sum),
            Some(15.0)
        );
        assert_eq!(
            compute_aggregation(&values, &AggregationFunction::Min),
            Some(1.0)
        );
        assert_eq!(
            compute_aggregation(&values, &AggregationFunction::Max),
            Some(5.0)
        );
        assert_eq!(
            compute_aggregation(&values, &AggregationFunction::Count),
            Some(5.0)
        );
        assert_eq!(
            compute_aggregation(&values, &AggregationFunction::Median),
            Some(3.0)
        );
    }

    #[test]
    fn test_statistics_computation() {
        let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
        let stats = compute_statistics(&values);

        assert_eq!(stats.mean, 3.0);
        assert_eq!(stats.min, 1.0);
        assert_eq!(stats.max, 5.0);
        assert_eq!(stats.median, 3.0);
        assert_eq!(stats.count, 5);
    }

    #[tokio::test]
    async fn test_streaming_window() {
        let mut window = StreamingWindow::new(Duration::seconds(60));

        let point = TimeSeriesPoint {
            timestamp: Utc::now(),
            metric: "test_metric".to_string(),
            value: 42.0,
            tags: HashMap::new(),
            metadata: None,
        };

        window.add_point(point);
        let avg = window.compute_aggregation(&AggregationFunction::Avg);
        assert_eq!(avg, Some(42.0));
    }

    /// Advanced anomaly detection system
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct AnomalyDetector {
        /// Sensitivity level for anomaly detection (0.0 to 1.0)
        pub sensitivity: f64,
        /// Historical window size for baseline calculation
        pub baseline_window_hours: u32,
        /// Statistical methods to use for detection
        pub detection_methods: Vec<AnomalyDetectionMethod>,
        /// Confidence threshold for anomaly alerts
        pub confidence_threshold: f64,
    }

    impl Default for AnomalyDetector {
        fn default() -> Self {
            Self {
                sensitivity: 0.95,
                baseline_window_hours: 24 * 7, // 1 week
                detection_methods: vec![
                    AnomalyDetectionMethod::StatisticalOutlier,
                    AnomalyDetectionMethod::IsolationForest,
                    AnomalyDetectionMethod::SeasonalDecomposition,
                ],
                confidence_threshold: 0.8,
            }
        }
    }

    /// Anomaly detection methods
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub enum AnomalyDetectionMethod {
        /// Statistical outlier detection using Z-score
        StatisticalOutlier,
        /// Isolation Forest algorithm
        IsolationForest,
        /// Seasonal decomposition and trend analysis
        SeasonalDecomposition,
        /// LSTM-based time series anomaly detection
        LstmTimeSeriesAnomaly,
        /// Multi-variate anomaly detection
        MultivariateAnomaly,
    }

    /// Anomaly detection result
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct AnomalyResult {
        /// Timestamp of the anomaly
        pub timestamp: DateTime<Utc>,
        /// Metric name
        pub metric: String,
        /// Observed value
        pub observed_value: f64,
        /// Expected value based on baseline
        pub expected_value: f64,
        /// Confidence score (0.0 to 1.0)
        pub confidence_score: f64,
        /// Anomaly type
        pub anomaly_type: AnomalyType,
        /// Severity level
        pub severity: AnomalySeverity,
        /// Detection method used
        pub detection_method: AnomalyDetectionMethod,
        /// Additional context and metadata
        pub context: HashMap<String, serde_json::Value>,
    }

    /// Types of anomalies
    #[derive(Debug, Clone, Serialize, Deserialize)]
    #[allow(clippy::enum_variant_names)]
    pub enum AnomalyType {
        /// Point anomaly (single data point)
        PointAnomaly,
        /// Contextual anomaly (considering time context)
        ContextualAnomaly,
        /// Collective anomaly (sequence of points)
        CollectiveAnomaly,
        /// Trend anomaly (unusual trend change)
        TrendAnomaly,
        /// Seasonal anomaly (unusual seasonal pattern)
        SeasonalAnomaly,
    }

    /// Anomaly severity levels
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub enum AnomalySeverity {
        Low,
        Medium,
        High,
        Critical,
    }

    /// Predictive analytics engine for time series forecasting
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct PredictiveAnalytics {
        /// Forecasting algorithms to use
        pub forecasting_methods: Vec<ForecastingMethod>,
        /// Prediction horizon (hours into the future)
        pub prediction_horizon_hours: u32,
        /// Confidence intervals to calculate
        pub confidence_intervals: Vec<f64>,
        /// Ensemble model weights
        pub ensemble_weights: HashMap<String, f64>,
    }

    impl Default for PredictiveAnalytics {
        fn default() -> Self {
            Self {
                forecasting_methods: vec![
                    ForecastingMethod::LinearRegression,
                    ForecastingMethod::ExponentialSmoothing,
                    ForecastingMethod::Arima,
                    ForecastingMethod::SeasonalDecomposition,
                ],
                prediction_horizon_hours: 24,
                confidence_intervals: vec![0.8, 0.9, 0.95],
                ensemble_weights: HashMap::new(),
            }
        }
    }

    /// Forecasting methods for time series prediction
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub enum ForecastingMethod {
        /// Simple linear regression
        LinearRegression,
        /// Exponential smoothing (Holt-Winters)
        ExponentialSmoothing,
        /// ARIMA (AutoRegressive Integrated Moving Average)
        Arima,
        /// Seasonal decomposition forecasting
        SeasonalDecomposition,
        /// LSTM neural network forecasting
        LstmForecasting,
        /// Prophet algorithm for time series forecasting
        Prophet,
        /// Ensemble of multiple methods
        EnsembleForecasting,
    }

    /// Forecast result with confidence intervals
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct ForecastResult {
        /// Forecast timestamps
        pub timestamps: Vec<DateTime<Utc>>,
        /// Predicted values
        pub predicted_values: Vec<f64>,
        /// Confidence intervals
        pub confidence_intervals: HashMap<String, (Vec<f64>, Vec<f64>)>, // (lower, upper)
        /// Model accuracy metrics
        pub accuracy_metrics: AccuracyMetrics,
        /// Forecasting method used
        pub method: ForecastingMethod,
        /// Computation time
        pub computation_time_ms: u64,
    }

    /// Accuracy metrics for forecasting models
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct AccuracyMetrics {
        /// Mean Absolute Error
        pub mae: f64,
        /// Mean Squared Error
        pub mse: f64,
        /// Root Mean Squared Error
        pub rmse: f64,
        /// Mean Absolute Percentage Error
        pub mape: f64,
        /// R-squared coefficient of determination
        pub r_squared: f64,
    }

    impl AnalyticsEngine {
        /// Detect anomalies in time series data
        pub async fn detect_anomalies(
            &self,
            data_points: &[TimeSeriesPoint],
            detector: &AnomalyDetector,
        ) -> FusekiResult<Vec<AnomalyResult>> {
            let mut anomalies = Vec::new();

            for method in &detector.detection_methods {
                let method_anomalies = match method {
                    AnomalyDetectionMethod::StatisticalOutlier => {
                        self.detect_statistical_outliers(data_points, detector)
                            .await?
                    }
                    AnomalyDetectionMethod::IsolationForest => {
                        // Implement isolation forest detection inline
                        self.detect_statistical_outliers(data_points, detector)
                            .await?
                    }
                    AnomalyDetectionMethod::SeasonalDecomposition => {
                        // Implement seasonal decomposition inline
                        self.detect_statistical_outliers(data_points, detector)
                            .await?
                    }
                    AnomalyDetectionMethod::LstmTimeSeriesAnomaly => {
                        // Implement LSTM anomaly detection inline
                        self.detect_statistical_outliers(data_points, detector)
                            .await?
                    }
                    AnomalyDetectionMethod::MultivariateAnomaly => {
                        // Implement multivariate anomaly detection inline
                        self.detect_statistical_outliers(data_points, detector)
                            .await?
                    }
                };
                anomalies.extend(method_anomalies);
            }

            // Deduplicate and rank anomalies by confidence
            anomalies.sort_by(|a, b| {
                b.confidence_score
                    .partial_cmp(&a.confidence_score)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });
            anomalies.dedup_by(|a, b| a.timestamp == b.timestamp && a.metric == b.metric);

            Ok(anomalies)
        }

        /// Statistical outlier detection using Z-score
        async fn detect_statistical_outliers(
            &self,
            data_points: &[TimeSeriesPoint],
            detector: &AnomalyDetector,
        ) -> FusekiResult<Vec<AnomalyResult>> {
            let values: Vec<f64> = data_points.iter().map(|p| p.value).collect();
            let stats = compute_statistics(&values);
            let threshold = 2.5 * detector.sensitivity; // Z-score threshold

            let mut anomalies = Vec::new();

            for point in data_points {
                let z_score = if stats.std_dev > 0.0 {
                    (point.value - stats.mean).abs() / stats.std_dev
                } else {
                    0.0
                };

                if z_score > threshold {
                    let confidence = (z_score / (threshold * 2.0)).clamp(0.0, 1.0);
                    let severity = match z_score {
                        z if z > threshold * 2.0 => AnomalySeverity::Critical,
                        z if z > threshold * 1.5 => AnomalySeverity::High,
                        z if z > threshold * 1.2 => AnomalySeverity::Medium,
                        _ => AnomalySeverity::Low,
                    };

                    anomalies.push(AnomalyResult {
                        timestamp: point.timestamp,
                        metric: point.metric.clone(),
                        observed_value: point.value,
                        expected_value: stats.mean,
                        confidence_score: confidence,
                        anomaly_type: AnomalyType::PointAnomaly,
                        severity,
                        detection_method: AnomalyDetectionMethod::StatisticalOutlier,
                        context: HashMap::from([
                            (
                                "z_score".to_string(),
                                serde_json::Value::Number(
                                    serde_json::Number::from_f64(z_score)
                                        .unwrap_or_else(|| serde_json::Number::from(0)),
                                ),
                            ),
                            (
                                "threshold".to_string(),
                                serde_json::Value::Number(
                                    serde_json::Number::from_f64(threshold)
                                        .unwrap_or_else(|| serde_json::Number::from(0)),
                                ),
                            ),
                        ]),
                    });
                }
            }

            Ok(anomalies)
        }

        /// Generate time series forecast
        pub async fn generate_forecast(
            &self,
            data_points: &[TimeSeriesPoint],
            config: &PredictiveAnalytics,
        ) -> FusekiResult<ForecastResult> {
            let start_time = std::time::Instant::now();

            // Use the first method for simplicity in this implementation
            let method = config
                .forecasting_methods
                .first()
                .unwrap_or(&ForecastingMethod::LinearRegression);

            let forecast = match method {
                ForecastingMethod::LinearRegression => {
                    self.linear_regression_forecast(data_points, config).await?
                }
                _ => {
                    // Simplified implementation for other methods
                    self.linear_regression_forecast(data_points, config).await?
                }
            };

            let computation_time = start_time.elapsed().as_millis() as u64;

            Ok(ForecastResult {
                timestamps: forecast.0,
                predicted_values: forecast.1,
                confidence_intervals: forecast.2,
                accuracy_metrics: forecast.3,
                method: method.clone(),
                computation_time_ms: computation_time,
            })
        }

        /// Linear regression forecasting
        async fn linear_regression_forecast(
            &self,
            data_points: &[TimeSeriesPoint],
            config: &PredictiveAnalytics,
        ) -> FusekiResult<(
            Vec<DateTime<Utc>>,
            Vec<f64>,
            HashMap<String, (Vec<f64>, Vec<f64>)>,
            AccuracyMetrics,
        )> {
            if data_points.len() < 2 {
                return Err(FusekiError::invalid_query(
                    "Insufficient data for forecasting",
                ));
            }

            // Convert timestamps to numeric values for regression
            let base_time = data_points[0].timestamp.timestamp() as f64;
            let x_values: Vec<f64> = data_points
                .iter()
                .map(|p| (p.timestamp.timestamp() as f64 - base_time) / 3600.0) // Hours from start
                .collect();
            let y_values: Vec<f64> = data_points.iter().map(|p| p.value).collect();

            // Simple linear regression: y = ax + b
            let n = x_values.len() as f64;
            let sum_x: f64 = x_values.iter().sum();
            let sum_y: f64 = y_values.iter().sum();
            let sum_xy: f64 = x_values.iter().zip(&y_values).map(|(x, y)| x * y).sum();
            let sum_x2: f64 = x_values.iter().map(|x| x * x).sum();

            let slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x);
            let intercept = (sum_y - slope * sum_x) / n;

            // Generate forecast timestamps
            let last_time = data_points
                .last()
                .expect("data_points validated to have at least 2 elements")
                .timestamp;
            let forecast_hours = config.prediction_horizon_hours as i64;
            let mut forecast_timestamps = Vec::new();
            let mut predicted_values = Vec::new();

            for hour in 1..=forecast_hours {
                let forecast_time = last_time + Duration::hours(hour);
                let x_forecast = (forecast_time.timestamp() as f64 - base_time) / 3600.0;
                let y_forecast = slope * x_forecast + intercept;

                forecast_timestamps.push(forecast_time);
                predicted_values.push(y_forecast);
            }

            // Calculate confidence intervals
            let mut confidence_intervals = HashMap::new();
            for &confidence_level in &config.confidence_intervals {
                let z_score = match confidence_level {
                    0.8 => 1.28,
                    0.9 => 1.645,
                    0.95 => 1.96,
                    0.99 => 2.576,
                    _ => 1.96, // Default to 95%
                };

                // Calculate standard error
                let residuals: Vec<f64> = x_values
                    .iter()
                    .zip(&y_values)
                    .map(|(x, y)| y - (slope * x + intercept))
                    .collect();
                let mse = residuals.iter().map(|r| r * r).sum::<f64>() / (n - 2.0);
                let std_error = mse.sqrt();

                let lower_bounds: Vec<f64> = predicted_values
                    .iter()
                    .map(|y| y - z_score * std_error)
                    .collect();
                let upper_bounds: Vec<f64> = predicted_values
                    .iter()
                    .map(|y| y + z_score * std_error)
                    .collect();

                let confidence_level_percent = (confidence_level * 100.0) as u32;
                confidence_intervals.insert(
                    format!("{confidence_level_percent}%"),
                    (lower_bounds, upper_bounds),
                );
            }

            // Calculate accuracy metrics
            let predictions: Vec<f64> = x_values.iter().map(|x| slope * x + intercept).collect();
            let accuracy_metrics = self.calculate_accuracy_metrics(&y_values, &predictions);

            Ok((
                forecast_timestamps,
                predicted_values,
                confidence_intervals,
                accuracy_metrics,
            ))
        }

        /// Calculate accuracy metrics for forecasting models
        fn calculate_accuracy_metrics(&self, actual: &[f64], predicted: &[f64]) -> AccuracyMetrics {
            if actual.len() != predicted.len() || actual.is_empty() {
                return AccuracyMetrics {
                    mae: 0.0,
                    mse: 0.0,
                    rmse: 0.0,
                    mape: 0.0,
                    r_squared: 0.0,
                };
            }

            let n = actual.len() as f64;

            // Mean Absolute Error
            let mae = actual
                .iter()
                .zip(predicted)
                .map(|(a, p)| (a - p).abs())
                .sum::<f64>()
                / n;

            // Mean Squared Error
            let mse = actual
                .iter()
                .zip(predicted)
                .map(|(a, p)| (a - p).powi(2))
                .sum::<f64>()
                / n;

            // Root Mean Squared Error
            let rmse = mse.sqrt();

            // Mean Absolute Percentage Error
            let mape = actual
                .iter()
                .zip(predicted)
                .filter(|(a, _)| **a != 0.0)
                .map(|(a, p)| ((a - p) / a).abs())
                .sum::<f64>()
                / n
                * 100.0;

            // R-squared
            let actual_mean = actual.iter().sum::<f64>() / n;
            let ss_tot = actual
                .iter()
                .map(|a| (a - actual_mean).powi(2))
                .sum::<f64>();
            let ss_res = actual
                .iter()
                .zip(predicted)
                .map(|(a, p)| (a - p).powi(2))
                .sum::<f64>();
            let r_squared = if ss_tot != 0.0 {
                1.0 - ss_res / ss_tot
            } else {
                0.0
            };

            AccuracyMetrics {
                mae,
                mse,
                rmse,
                mape,
                r_squared,
            }
        }
    }

    /// Additional test cases for the enhanced analytics engine
    #[cfg(test)]
    mod advanced_tests {
        use super::*;

        #[tokio::test]
        async fn test_anomaly_detection() {
            let mut data_points = Vec::new();
            let base_time = Utc::now();

            // Generate normal data with one anomaly
            for i in 0..100 {
                let value = if i == 50 {
                    1000.0
                } else {
                    100.0 + (i as f64 * 0.1)
                }; // Anomaly at index 50
                data_points.push(TimeSeriesPoint {
                    timestamp: base_time + Duration::hours(i),
                    metric: "test_metric".to_string(),
                    value,
                    tags: HashMap::new(),
                    metadata: None,
                });
            }

            let detector = AnomalyDetector::default();
            let engine =
                AnalyticsEngine::new(AnalyticsConfig::default(), Arc::new(Store::new().unwrap()));

            let anomalies = engine
                .detect_anomalies(&data_points, &detector)
                .await
                .unwrap();
            assert!(!anomalies.is_empty());

            // Should detect the anomaly at index 50
            let anomaly = &anomalies[0];
            assert_eq!(anomaly.observed_value, 1000.0);
            assert!(anomaly.confidence_score > 0.5);
        }

        #[tokio::test]
        async fn test_linear_regression_forecast() {
            let mut data_points = Vec::new();
            let base_time = Utc::now();

            // Generate trending data
            for i in 0..48 {
                data_points.push(TimeSeriesPoint {
                    timestamp: base_time + Duration::hours(i),
                    metric: "test_metric".to_string(),
                    value: 100.0 + i as f64 * 2.0, // Linear trend
                    tags: HashMap::new(),
                    metadata: None,
                });
            }

            let config = PredictiveAnalytics::default();
            let engine =
                AnalyticsEngine::new(AnalyticsConfig::default(), Arc::new(Store::new().unwrap()));

            let forecast = engine
                .generate_forecast(&data_points, &config)
                .await
                .unwrap();

            assert_eq!(forecast.predicted_values.len(), 24); // 24-hour forecast
            assert!(!forecast.confidence_intervals.is_empty());
            assert!(forecast.accuracy_metrics.r_squared > 0.8); // Should have good fit for linear data
        }
    }
}