quantrs2-device 0.1.3

//! Time series analysis components

use super::super::results::*;
use crate::DeviceResult;
use scirs2_core::ndarray::Array1;

/// Time series analyzer for measurement data
pub struct TimeSeriesAnalyzer {
    // Configuration and state
}

impl TimeSeriesAnalyzer {
    /// Create new time series analyzer
    pub const fn new() -> Self {
        Self {}
    }

    /// Perform time series analysis
    pub fn analyze(
        &self,
        values: &[f64],
        timestamps: &[f64],
    ) -> DeviceResult<TimeSeriesAnalysisResults> {
        if values.is_empty() || timestamps.is_empty() {
            return Ok(TimeSeriesAnalysisResults {
                trend_analysis: TrendAnalysis::default(),
                seasonality_analysis: None,
                autocorrelation: AutocorrelationAnalysis::default(),
                change_points: vec![],
                stationarity: StationarityTestResults::default(),
            });
        }

        let trend_analysis = self.analyze_trend(values, timestamps)?;
        let seasonality_analysis = self.analyze_seasonality(values).ok_or_else(|| {
            crate::DeviceError::APIError("Failed to analyze seasonality".to_string())
        })?;
        let autocorrelation = self.analyze_autocorrelation(values)?;
        let change_points = self.detect_change_points(values, timestamps)?;
        let stationarity = self.test_stationarity(values)?;

        Ok(TimeSeriesAnalysisResults {
            trend_analysis,
            seasonality_analysis: Some(seasonality_analysis),
            autocorrelation,
            change_points,
            stationarity,
        })
    }

    /// Analyze trend in time series
    fn analyze_trend(&self, values: &[f64], timestamps: &[f64]) -> DeviceResult<TrendAnalysis> {
        if values.len() < 2 {
            return Ok(TrendAnalysis::default());
        }

        // Simple linear regression for trend
        let n = values.len() as f64;
        let mean_x = timestamps.iter().sum::<f64>() / n;
        let mean_y = values.iter().sum::<f64>() / n;

        let mut numerator = 0.0;
        let mut denominator = 0.0;

        for i in 0..values.len() {
            let x_diff = timestamps[i] - mean_x;
            let y_diff = values[i] - mean_y;
            numerator += x_diff * y_diff;
            denominator += x_diff * x_diff;
        }

        let trend_slope = if denominator > 1e-10 {
            numerator / denominator
        } else {
            0.0
        };

        // Determine trend direction
        let trend_direction = if trend_slope > 0.01 {
            TrendDirection::Increasing
        } else if trend_slope < -0.01 {
            TrendDirection::Decreasing
        } else {
            TrendDirection::Stable
        };

        // Calculate trend strength (R-squared)
        let mut ss_tot = 0.0;
        let mut ss_res = 0.0;
        let intercept = mean_y - trend_slope * mean_x;

        for i in 0..values.len() {
            let predicted = trend_slope * timestamps[i] + intercept;
            ss_res += (values[i] - predicted).powi(2);
            ss_tot += (values[i] - mean_y).powi(2);
        }

        let trend_strength = if ss_tot > 1e-10 {
            1.0 - (ss_res / ss_tot)
        } else {
            0.0
        };

        let trend_significance = if trend_strength > 0.5 { 0.01 } else { 0.1 };
        let trend_ci = (trend_slope - 0.1, trend_slope + 0.1); // Simplified CI

        Ok(TrendAnalysis {
            trend_direction,
            trend_strength,
            trend_slope,
            trend_significance,
            trend_ci,
        })
    }

    /// Analyze seasonality
    fn analyze_seasonality(&self, values: &[f64]) -> Option<SeasonalityAnalysis> {
        if values.len() < 10 {
            return None;
        }

        // Simple seasonality detection
        let periods = vec![7, 14, 30]; // Common periods
        let seasonal_strength = 0.3; // Placeholder
        let seasonal_components = Array1::zeros(values.len());
        let residual_components = Array1::from_vec(values.to_vec());

        Some(SeasonalityAnalysis {
            periods,
            seasonal_strength,
            seasonal_components,
            residual_components,
        })
    }

    /// Analyze autocorrelation
    fn analyze_autocorrelation(&self, values: &[f64]) -> DeviceResult<AutocorrelationAnalysis> {
        if values.len() < 3 {
            return Ok(AutocorrelationAnalysis::default());
        }

        let max_lag = (values.len() / 4).min(20);
        let mut acf = Vec::with_capacity(max_lag);
        let mut pacf = Vec::with_capacity(max_lag);

        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;

        // Calculate autocorrelation function
        for lag in 1..=max_lag {
            let mut covariance = 0.0;
            let mut count = 0;

            for i in lag..values.len() {
                covariance += (values[i] - mean) * (values[i - lag] - mean);
                count += 1;
            }

            let correlation = if variance > 1e-10 && count > 0 {
                (covariance / count as f64) / variance
            } else {
                0.0
            };

            acf.push(correlation);
            pacf.push(correlation); // Simplified - should calculate partial autocorrelation
        }

        // Find significant lags (simplified)
        let threshold = 2.0 / (values.len() as f64).sqrt();
        let significant_lags: Vec<usize> = acf
            .iter()
            .enumerate()
            .filter(|(_, &corr)| corr.abs() > threshold)
            .map(|(i, _)| i + 1)
            .collect();

        // Ljung-Box test (simplified)
        let ljung_box_statistic =
            acf.iter().take(10).map(|&x| x.powi(2)).sum::<f64>() * values.len() as f64;
        let ljung_box_p_value = if ljung_box_statistic > 18.3 {
            0.01
        } else {
            0.1
        };

        Ok(AutocorrelationAnalysis {
            acf: Array1::from_vec(acf),
            pacf: Array1::from_vec(pacf),
            significant_lags,
            ljung_box_statistic,
            ljung_box_p_value,
        })
    }

    /// Detect change points
    fn detect_change_points(
        &self,
        values: &[f64],
        timestamps: &[f64],
    ) -> DeviceResult<Vec<ChangePoint>> {
        if values.len() < 10 {
            return Ok(vec![]);
        }

        let mut change_points = Vec::new();
        let window_size = values.len() / 5;

        // Simple change point detection using moving averages
        for i in window_size..(values.len() - window_size) {
            let before_mean = values[(i - window_size)..i].iter().sum::<f64>() / window_size as f64;
            let after_mean = values[i..(i + window_size)].iter().sum::<f64>() / window_size as f64;

            let magnitude = (after_mean - before_mean).abs();

            if magnitude > 0.1 {
                // Threshold for change detection
                change_points.push(ChangePoint {
                    index: i,
                    timestamp: timestamps[i],
                    confidence: 0.8,
                    magnitude,
                    change_type: if after_mean > before_mean {
                        ChangePointType::MeanShift
                    } else {
                        ChangePointType::MeanShift
                    },
                });
            }
        }

        Ok(change_points)
    }

    /// Test stationarity
    const fn test_stationarity(&self, values: &[f64]) -> DeviceResult<StationarityTestResults> {
        // Simplified stationarity tests
        let adf_test = StatisticalTest {
            statistic: -2.5,
            p_value: 0.05,
            critical_value: -2.86,
            is_significant: true,
            effect_size: Some(0.3),
        };

        let kpss_test = StatisticalTest {
            statistic: 0.3,
            p_value: 0.1,
            critical_value: 0.463,
            is_significant: false,
            effect_size: Some(0.2),
        };

        let pp_test = StatisticalTest {
            statistic: -3.0,
            p_value: 0.03,
            critical_value: -2.86,
            is_significant: true,
            effect_size: Some(0.25),
        };

        let is_stationary = adf_test.is_significant && !kpss_test.is_significant;

        Ok(StationarityTestResults {
            adf_test,
            kpss_test,
            pp_test,
            is_stationary,
        })
    }
}

impl Default for TimeSeriesAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

impl Default for TrendAnalysis {
    fn default() -> Self {
        Self {
            trend_direction: TrendDirection::Stable,
            trend_strength: 0.0,
            trend_slope: 0.0,
            trend_significance: 0.1,
            trend_ci: (0.0, 0.0),
        }
    }
}

impl Default for AutocorrelationAnalysis {
    fn default() -> Self {
        Self {
            acf: Array1::zeros(0),
            pacf: Array1::zeros(0),
            significant_lags: vec![],
            ljung_box_statistic: 0.0,
            ljung_box_p_value: 0.1,
        }
    }
}

impl Default for StationarityTestResults {
    fn default() -> Self {
        Self {
            adf_test: StatisticalTest::default(),
            kpss_test: StatisticalTest::default(),
            pp_test: StatisticalTest::default(),
            is_stationary: true,
        }
    }
}