scirs2_integrate/analysis/ml_prediction/
forecasting.rs

1//! Time Series Forecasting for Bifurcation Prediction
2//!
3//! This module contains structures and configurations for time series forecasting,
4//! anomaly detection, and trend analysis in dynamical systems.
5
6/// Time series forecasting for bifurcation prediction
7#[derive(Debug, Clone)]
8pub struct TimeSeriesBifurcationForecaster {
9    /// Base time series model
10    pub base_model: TimeSeriesModel,
11    /// Bifurcation detection threshold
12    pub detection_threshold: f64,
13    /// Forecast horizon
14    pub forecast_horizon: usize,
15    /// Multi-step forecasting strategy
16    pub multistep_strategy: MultiStepStrategy,
17    /// Anomaly detection configuration
18    pub anomaly_detection: AnomalyDetectionConfig,
19    /// Trend analysis configuration
20    pub trend_analysis: TrendAnalysisConfig,
21}
22
23/// Time series model types
24#[derive(Debug, Clone)]
25pub enum TimeSeriesModel {
26    /// LSTM-based model
27    LSTM {
28        hidden_size: usize,
29        num_layers: usize,
30        bidirectional: bool,
31    },
32    /// GRU-based model
33    GRU {
34        hidden_size: usize,
35        num_layers: usize,
36        bidirectional: bool,
37    },
38    /// Transformer-based model
39    Transformer {
40        d_model: usize,
41        nhead: usize,
42        num_layers: usize,
43        positional_encoding: bool,
44    },
45    /// Conv1D-based model
46    Conv1D {
47        channels: Vec<usize>,
48        kernel_sizes: Vec<usize>,
49        dilations: Vec<usize>,
50    },
51    /// Hybrid CNN-RNN model
52    HybridCNNRNN {
53        cnn_channels: Vec<usize>,
54        rnn_hidden_size: usize,
55        rnn_layers: usize,
56    },
57}
58
59/// Multi-step forecasting strategies
60#[derive(Debug, Clone, Copy)]
61pub enum MultiStepStrategy {
62    /// Recursive one-step ahead
63    Recursive,
64    /// Direct multi-step
65    Direct,
66    /// Multi-input multi-output
67    MIMO,
68    /// Ensemble of strategies
69    Ensemble,
70}
71
72/// Anomaly detection configuration
73#[derive(Debug, Clone)]
74pub struct AnomalyDetectionConfig {
75    /// Anomaly detection method
76    pub method: AnomalyDetectionMethod,
77    /// Threshold for anomaly detection
78    pub threshold: f64,
79    /// Window size for anomaly detection
80    pub window_size: usize,
81    /// Minimum anomaly duration
82    pub min_duration: usize,
83}
84
85/// Anomaly detection methods
86#[derive(Debug, Clone, Copy)]
87pub enum AnomalyDetectionMethod {
88    /// Statistical outlier detection
89    StatisticalOutlier,
90    /// Isolation forest
91    IsolationForest,
92    /// One-class SVM
93    OneClassSVM,
94    /// Autoencoder-based detection
95    Autoencoder,
96    /// LSTM-based prediction error
97    LSTMPredictionError,
98}
99
100/// Trend analysis configuration
101#[derive(Debug, Clone)]
102pub struct TrendAnalysisConfig {
103    /// Trend detection method
104    pub method: TrendDetectionMethod,
105    /// Trend analysis window size
106    pub window_size: usize,
107    /// Significance level for trend tests
108    pub significance_level: f64,
109    /// Change point detection
110    pub change_point_detection: bool,
111}
112
113/// Trend detection methods
114#[derive(Debug, Clone, Copy)]
115pub enum TrendDetectionMethod {
116    /// Linear regression slope
117    LinearRegression,
118    /// Mann-Kendall test
119    MannKendall,
120    /// Sen's slope estimator
121    SensSlope,
122    /// Seasonal Mann-Kendall
123    SeasonalMannKendall,
124    /// CUSUM test
125    CUSUM,
126}
scirs2_integrate/analysis/ml_prediction/forecasting.rs

scirs2_integrate/analysis/ml_prediction/
forecasting.rs
