scirs2-series

Comprehensive time series analysis for Rust — part of the SciRS2 scientific computing ecosystem.

scirs2-series is a large time series library covering classical econometric models through state-of-the-art deep learning forecasters: the neural architecture forecaster suite (TFT, N-BEATS, N-HiTS, DeepAR), streaming/online algorithms, long-memory processes, intermittent demand forecasting, and hierarchical reconciliation are all real and reachable through the public API. A handful of advertised areas are weaker than the rest of the crate — see TODO.md "Known Issues" for specifics (most notably: split/adaptive conformal prediction code exists in the source tree but is not yet wired into lib.rs, so it is not reachable from outside the crate; only Markov-switching autoregression is implemented among the regime-switching models; and VECMModel::fit is currently a placeholder that does not run the Johansen procedure). Everything else described below has been spot-checked against src/.

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Overview

Time series problems span a wide spectrum: univariate forecasting with uncertainty quantification, multivariate causal modelling, streaming anomaly detection, hierarchical forecasting across organizational hierarchies, regime detection, and functional data analysis. scirs2-series covers all of these in a unified, type-safe API.

Key design goals:

• Breadth: classical (ARIMA, ETS) through neural (TFT, N-BEATS, DeepAR) through streaming (online ARIMA, ADWIN)
• Uncertainty quantification: confidence/prediction intervals on ARIMA and other forecasters, probabilistic neural outputs (DeepAR), and evaluation-side coverage/Winkler-score diagnostics
• Ecosystem coherence: built on scirs2-core abstractions; no C/Fortran dependencies
• Performance: SIMD-accelerated operations via scirs2-core (simd feature); the crate has no rayon dependency — the out_of_core module instead uses std::thread directly for its own worker pool

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Feature List (v0.6.2)

Decomposition

• STL (Seasonal-Trend decomposition using Loess) with robustness iterations
• TBATS (Trigonometric seasonality, Box-Cox, ARMA errors, Trend, Seasonal)
• SSA (Singular Spectrum Analysis) with grouping and reconstruction
• STR (Seasonal-Trend decomposition with Regression)
• Multi-seasonal decomposition for complex seasonal patterns
• Classical additive and multiplicative decomposition
• Robust variants with outlier handling

Forecasting: Classical & Statistical

• ARIMA / SARIMA with Auto-ARIMA (stepwise and grid search)
• Exponential smoothing: Simple ES, Holt's linear trend, Holt-Winters, ETS framework
• BATS / TBATS for complex multi-seasonal data
• Theta method and Theta-F variants
• Naive, seasonal naive, drift, moving average, and ensemble of simple methods
• Intermittent demand: Croston's method, Syntetos-Boylan Approximation (SBA), TSB (Teunter-Syntetos-Babai)

Forecasting: Neural Architectures

• Temporal Fusion Transformer (TFT): multi-horizon attention-based model with variable selection, gating, and static covariate encoding
• N-BEATS: neural basis expansion for interpretable time series forecasting (trend and seasonality stacks)
• N-HiTS: hierarchical interpolation with multi-rate signal sampling
• DeepAR: autoregressive RNN with probabilistic output (Gaussian, negative binomial) for Amazon-style probabilistic forecasting
• Simple neural forecast API: common interface across all neural models

State-Space Models & Kalman Filtering

• Kalman filter and Rauch-Tung-Striebel smoother
• Extended Kalman filter (EKF) for nonlinear systems
• Unscented Kalman filter (UKF) with sigma-point propagation
• Structural time series (local level, local linear trend, seasonal, cycle)
• Unobserved components models
• Dynamic linear models with time-varying parameters

Volatility & GARCH Models

• GARCH(p,q) and EGARCH (exponential GARCH)
• FIGARCH (fractionally integrated GARCH) for long-memory volatility
• GJR-GARCH (asymmetric leverage effects)
• ARCH-LM test, Ljung-Box test for model diagnostics

Long-Memory Processes

• ARFIMA (Autoregressive Fractionally Integrated Moving Average) estimation and simulation
• Hurst exponent estimation: R/S analysis, detrended fluctuation analysis (DFA), Whittle estimator
• Fractional differencing (fractional-d operator) with memory-preserving transforms

Causality & Cointegration

• Granger causality testing with F-statistics and block-exogeneity (causality::granger_causality_test), spectral (frequency-domain) Granger causality, conditional/multivariate variants
• Transfer entropy (Shannon, Renyi, conditional, effective) with bootstrap significance testing
• Convergent cross mapping (CCM) for nonlinear causality
• Causal discovery: PC, PC-stable, PCMCI, FCI (latent confounders)
• Johansen trace cointegration test over a rolling window (streaming::cointegration::StreamingCointegrationTester); a simplified Engle-Granger residual-based check is used internally by the ECM regression fitting path
• VECMModel type exists in var_models, but VECMModel::fit is currently a placeholder that does not run the Johansen estimation procedure — see Known Issues in TODO.md

Vector Autoregressive (VAR) Models

• VAR(p) fitting with OLS and information criterion lag selection (AIC, BIC, HQIC)
• Impulse response functions (IRF, VARModel::impulse_response) and forecast error variance decomposition (FEVD, VARModel::variance_decomposition); bootstrap confidence bands around the IRF are not implemented
• Granger causality block-exogeneity Wald test — real and reachable via causality::granger_causality_test and friends. Caveat: the separate convenience method VARModel::granger_causality(&self, ...) is currently hardcoded (f_stat = 2.5, p_value = 0.05 regardless of input — comment says "Would implement proper F-test for coefficient restrictions"); use the causality module function instead
• VECM for cointegrated systems

Functional Data Analysis (FDA)

• Functional PCA (dimensionality_reduction::functional_pca), including bivariate and multilevel variants
• Dynamic time warping barycenter averaging (DBA) with medoid/mean initialization (dimensionality_reduction::dtw)
• A richer FDA suite (B-spline/Fourier/wavelet basis expansions, functional linear model, functional ANOVA, k-centres functional clustering) exists as source files under src/functional/ but is not currently declared as a module in lib.rs, so it is not part of the public API — see Known Issues in TODO.md

Hierarchical Forecasting & Reconciliation

• Bottom-up, top-down (average historical proportions, PHA, TDA), and middle-out aggregation
• Optimal reconciliation: MinT (trace minimisation), WLS (weighted least squares), OLS
• Cross-temporal reconciliation for multi-frequency hierarchies
• Evaluation with hierarchical MASE and weighted MAPE

Conformal Prediction for Time Series (not yet exposed publicly)

• Split conformal, EnbPI, adaptive/weighted conformal, and interval calibration diagnostics have source code under src/conformal/ and src/forecast_uncertainty/, but neither directory is declared as a module in lib.rs, so none of this is reachable as scirs2_series::... today
• Mondrian conformal prediction does not exist anywhere in the source tree
• See Known Issues in TODO.md for the wiring gap

Online / Streaming Algorithms

• ADWIN (Adaptive Windowing) concept drift detector (online_algorithms::ADWINDetector)
• Online ARIMA with recursive-least-squares-style parameter tracking
• Streaming quantile estimation via the P² algorithm (online_algorithms::OnlineQuantile); a KLL sketch is not implemented
• Online anomaly detection: CUSUM, EWMA control charts, streaming isolation forest
• Reservoir sampling (uniform and weighted) and sliding window statistics

Change Detection

• PELT (Pruned Exact Linear Time) for multiple change point detection
• Binary segmentation (greedy and exact variants)
• CUSUM (cumulative sum) control charts
• Bayesian online change point detection (BOCPD)
• Kernel-based change detection (MMD statistics)

Anomaly Detection

• Statistical process control (SPC): Shewhart, CUSUM, EWMA charts
• Z-score and modified Z-score methods
• IQR-based detection
• Isolation forest adapted for time series
• Prediction-error-based and reconstruction-based anomaly scores
• Distance-based approaches (matrix profile, LOF)

Pattern Analysis

• Autocorrelation (ACF) and partial autocorrelation (PACF) with confidence bands
• Cross-correlation with bootstrap confidence intervals
• Dynamic time warping (DTW) with Sakoe-Chiba and Itakura constraints
• Symbolic Aggregate approXimation (SAX), APCA, PLA, and Persist — dimensionality_reduction::symbolic::{apply_symbolic_approximation, SymbolicMethod}
• Time-frequency analysis: STFT, CWT (Morlet), coherence analysis (correlation, features::wavelet)
• Motif discovery and discord detection via matrix profile has source code (TimeSeriesMotif, Discord) in src/pattern/mod.rs, but — like src/conformal/, src/forecast_uncertainty/, src/functional/, and src/change_detection/ — that directory is not declared as a module in lib.rs, so it is not reachable from the public API; see Known Issues in TODO.md

Feature Engineering (60+ features)

• Statistical: mean, variance, skewness, kurtosis, entropy, crossing rate, linearity
• Frequency domain: spectral entropy, spectral centroid, dominant frequency (features::frequency); a further "noise bandwidth" field exists on SpectralAnalysisFeatures but is only ever zero-initialized, never actually computed, in the current code
• Complexity: approximate entropy, sample entropy, permutation entropy, Lyapunov exponent estimate
• Trend: linear trend slope, Hurst exponent, CUSUM range, range/IQR ratio
• Lag-based: ACF at specified lags, PACF, partial correlation coefficients
• Automated selection: filter, wrapper (forward/backward), embedded (LASSO, tree importance)

Regression Models for Time Series

• Distributed lag (DL) models with flexible lag structures
• Autoregressive distributed lag (ARDL) with automatic lag selection
• Error correction models (ECM) for cointegrated series
• Regression with ARIMA errors (ARIMAX / REGARIMA)

Clustering & Classification

• Time series clustering: k-means, hierarchical, DBSCAN, spectral, and Gaussian mixture models (clustering::ClusteringAlgorithm)
• k-NN classification with DTW, Euclidean, and correlation-based distances; feature-based and ensemble classification
• Shapelet discovery and shapelet transform classification
• k-medoids (PAM) and HDBSCAN are not implemented as distinct algorithms; functional-data clustering (k-centres functional) exists only in the unwired src/functional/ tree (see FDA note above)

Ensemble & Probabilistic Forecasting

• Ensemble forecasting (ensemble_forecast): simple average, weighted average, stacking, median ensemble, trimmed mean, bagging, dynamic ensemble selection
• Probabilistic forecast evaluation (energy_forecast): pinball loss, CRPS, coverage, Winkler score, reliability diagrams, sharpness, skill score
• General-purpose point-forecast evaluation (evaluation): MAE, MAPE, SMAPE, MASE (incl. seasonal), RMSE, MSE, WAPE, coverage probability, Winkler score, Diebold-Mariano test
• Bootstrap/conformal prediction intervals and quantile regression forests, and log score / PIT histograms specifically, are not currently implemented anywhere in the crate

Domain-Specific Extensions

• Financial: GARCH volatility, 15+ technical indicators (CCI, MFI, OBV, Parabolic SAR, RSI, MACD, Bollinger Bands, ATR)
• Environmental: heat wave detection, SPI drought index, growing degree days, SOI/NAO climate indices
• Biomedical: ECG R-peak detection, HRV analysis, EEG frequency band decomposition, EMG onset detection
• IoT sensors: environmental sensor fusion, GPS activity recognition, predictive maintenance scoring

Transformations

• Box-Cox transformation with automatic lambda estimation
• Differencing (regular and seasonal), fractional differencing
• Normalization: Z-score, Min-Max, robust (median/IQR)
• Stationarity transformation pipeline with ADF/KPSS guidance

Regime-Switching Models

• Markov-switching autoregression (MS-AR) with Hamilton filter, Kim smoother, and EM estimation (regime::fit_msar)
• Simple CUSUM-of-innovations structural break detection on a fitted state-space model (state_space::detect_structural_break_ssm)
• Threshold/self-exciting (TAR/SETAR), smooth-transition (STAR), and Bai-Perron multiple structural break tests are not implemented anywhere in the crate despite being listed in earlier TODO notes — see Known Issues in TODO.md

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Quick Start

[dependencies]
scirs2-series = "0.6.2"

ARIMA Forecasting

use scirs2_series::arima_models::arima;
use scirs2_core::ndarray::Array1;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let data: Array1<f64> = Array1::from(vec![
        110.0, 115.0, 118.0, 122.0, 120.0, 125.0, 130.0, 128.0,
        132.0, 135.0, 140.0, 138.0, 145.0, 148.0, 152.0, 155.0,
    ]);

    let model = arima(&data, 1, 1, 1)?;
    let forecast = model.forecast_with_confidence(5, &data, 0.95)?;
    println!("5-step forecast: {:?}", forecast.point_forecast);
    println!("95% interval:    {:?} .. {:?}", forecast.lower_ci, forecast.upper_ci);
    Ok(())
}

Temporal Fusion Transformer

use scirs2_series::tft::{TFT, TFTConfig};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = TFTConfig {
        hidden_size: 8,
        n_heads: 2,
        dropout: 0.0,
        horizon: 4,
        lookback: 12,
    };
    let model = TFT::new(config);

    let x_past: Vec<Vec<f32>> = vec![vec![0.5f32]; 12];
    let x_future: Vec<Vec<f32>> = vec![vec![0.5f32]; 4];
    let forecast = model.forward(&x_past, &x_future)?;
    println!("forecast: {:?}", forecast);
    Ok(())
}

(There is a separate, simpler TFTModel in neural_forecast::tft used by the "simple neural forecast API"; the example above uses the standalone scirs2_series::tft module, which also carries the FlashAttention-for-long-lookback variant.)

Granger Causality Test

use scirs2_series::causality::granger_causality_test;
use scirs2_core::ndarray::array;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let x = array![1.0f64, 1.5, 2.0, 2.5, 3.0, 3.2, 2.8, 3.5, 4.0, 3.7];
    let y = array![0.5f64, 0.8, 1.2, 1.8, 2.2, 2.6, 2.4, 2.9, 3.3, 3.1];

    let result = granger_causality_test(&x, &y, 2)?;
    println!("F-statistic: {:.4}, p-value: {:.4}", result.f_statistic, result.p_value);
    println!("Does x Granger-cause y? {}", result.p_value < 0.05);
    Ok(())
}

ADWIN Concept Drift Detection

use scirs2_series::online_algorithms::ADWINDetector;

let mut detector = ADWINDetector::new(0.002); // delta parameter

for &obs in &stream_of_values {
    if detector.update(obs) {
        println!("Concept drift detected at this point!");
    }
}

Hierarchical Reconciliation (MinT)

use scirs2_series::reconciliation::{SummationMatrix, MinTraceReconciliation};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Node 0 is the aggregate total; nodes 1 and 2 are its two bottom-level children.
    let parents: Vec<Option<usize>> = vec![None, Some(0), Some(0)];
    let s = SummationMatrix::from_parents(&parents)?;

    let reconciled =
        MinTraceReconciliation::reconcile_mint_shrink(&base_forecasts, &s, &residuals)?;
    println!("{:?}", reconciled);
    Ok(())
}

Note: split/adaptive conformal prediction intervals are not shown here because that code (src/conformal/, src/forecast_uncertainty/) is not currently wired into lib.rs — see Known Issues in TODO.md.

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API Overview

Module	Description
arima_models	ARIMA, SARIMA, Auto-ARIMA, ARIMAX
ets	ETS (Error-Trend-Seasonal) exponential smoothing framework
bats / tbats	BATS and TBATS multi-seasonal models
theta	Theta method and Theta-F
intermittent	Croston, SBA, TSB for intermittent demand
neural_forecast	TFT, N-BEATS, N-HiTS, DeepAR, simple API
state_space	Kalman filter, EKF, UKF, structural time series
forecasting	Naive, drift, MA, ensemble of simple methods
var_models	VAR, impulse response, variance decomposition; VECMModel type is present but fit() is a placeholder
causality	Granger causality (incl. spectral/conditional), transfer entropy, CCM, PC/PC-stable/PCMCI/FCI causal discovery
streaming::cointegration	Rolling-window Johansen trace cointegration test
volatility	GARCH, EGARCH, FIGARCH, GJR-GARCH
long_memory	ARFIMA, Hurst estimation, fractional differencing
decomposition	STL, SSA, STR, TBATS, classical
features	60+ time series features with automated selection
feature_selection	Filter, wrapper, embedded feature selection
change_point	Unified PELT / binary segmentation / CUSUM / Bayesian-online / kernel change-point dispatch; PELT also available standalone via detection::pelt
anomaly	SPC charts, isolation forest, prediction-error methods
streaming	Streaming statistics, change detection, cointegration (ADWIN itself lives in online_algorithms)
online_algorithms	ADWIN drift detector, P² streaming quantiles, online regression
conformal	Not wired into lib.rs — unreachable (see Known Issues)
hierarchical	Hierarchical aggregation strategies; hierarchical::reconciliation also has its own HierarchyMatrix/MinT/bottom-up/ERM functions
reconciliation	MinT (shrinkage), WLS, OLS optimal reconciliation, summation-matrix builder
ensemble_forecast	Forecast combination, stacking, bagging, dynamic ensemble selection
regime	Markov-switching AR (Hamilton filter) only — TAR/SETAR/STAR are not implemented
structural	Structural time series models (local level, local linear trend) with Kalman decompose/forecast — not a Bai-Perron break test
dimensionality_reduction	Functional PCA, DTW + barycenter averaging, other reduction utilities
clustering	k-means, hierarchical, DBSCAN, spectral, GMM clustering; DTW/kNN/shapelet classification
correlation	ACF, PACF, CCF, DTW, coherence
regression	DL, ARDL, ECM, regression with ARIMA errors
transformations	Box-Cox, differencing, normalization, stationarity
tests	Unit root and stationarity tests (ADF, KPSS, PP)
evaluation	MAE, MAPE, SMAPE, MASE, RMSE, WAPE, coverage, Winkler score, Diebold-Mariano
energy_forecast	Pinball loss, CRPS, reliability diagrams, sharpness, skill score (in addition to its energy-domain forecasting)
financial	Technical indicators, GARCH, financial metrics
environmental	Climate indices, drought, weather analysis
biomedical	ECG, EEG, EMG signal analysis
iot_sensors	Sensor fusion, predictive maintenance

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Feature Flags

Flag	Description
simd	SIMD-accelerated operations via scirs2-core
serde	Serialization support
wasm	WebAssembly bindings (pulls in serde)
python	Python interop layer (via scirs2-core/python)
r	R integration (r_integration module)

default = [] — no feature is enabled by default. There is no parallel feature; the crate does not depend on rayon.

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Testing

grep -rn "todo!()\|unimplemented!()" src/ returns 0 matches — but see TODO.md "Known Issues" for several public API methods that silently return placeholder/fabricated results instead of panicking or erroring, which a todo!() grep cannot catch.

Freshly measured 2026-07-15:

cargo nextest run -p scirs2-series               # default features: 1752 tests run, 1752 passed, 1 skipped
cargo nextest run -p scirs2-series --all-features # all-features:     1805 tests run, 1805 passed, 1 skipped

Both runs: 0 failed. (The 1 skipped test is #[ignore]d in both modes.)

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Links

• SciRS2 project
• docs.rs
• crates.io
• TODO.md

License

Apache License 2.0. See LICENSE for details.

Authors

COOLJAPAN OU (Team KitaSan)