SciRS2 Series

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Production-ready time series analysis module for the SciRS2 scientific computing library (v0.1.0). Following the SciRS2 POLICY, this Stable Release provides comprehensive, tested, and optimized tools for analyzing, decomposing, and forecasting time series data with feature parity to leading Python libraries, zero-warning code quality, and ecosystem consistency through scirs2-core abstractions.

Features

Core Capabilities:

• Advanced Decomposition: STL, TBATS, SSA, STR, Multi-seasonal decomposition, Robust variants
• State-of-the-Art Forecasting: ARIMA/SARIMA, Auto-ARIMA, Exponential Smoothing (ETS), Holt-Winters
• Comprehensive Analysis: Autocorrelation, partial autocorrelation, cross-correlation functions
• Change Point & Anomaly Detection: PELT, CUSUM, Bayesian online detection, multiple anomaly methods
• Feature Engineering: 60+ statistical, frequency, and complexity features with automated selection
• Transformations: Box-Cox, differencing, normalization, stationarity tests
• State-Space Models: Kalman filtering, structural time series, dynamic linear models
• Causality Analysis: Granger causality, transfer entropy, causal impact analysis
• Dimensionality Reduction: PCA, functional PCA, symbolic approximation
• Clustering & Classification: Time series clustering, DTW-based methods, shapelet discovery

Production Features:

• Zero-warning Codebase: All clippy warnings resolved, production-ready code quality
• Comprehensive Testing: 137 unit tests, 43 doc tests, 100% core functionality coverage
• Performance Optimized: SIMD acceleration, parallel processing, memory-efficient algorithms
• Rust Ecosystem Integration: Full ndarray support, compatible with scientific Rust ecosystem

Installation

Stable Release - Production Ready

Add the following to your Cargo.toml:

[dependencies]
scirs2-series = "0.1.2"

Recommended for Production: Enable performance optimizations:

[dependencies]
scirs2-series = { version = "0.1.2", features = ["parallel", "simd"] }
scirs2-core = { version = "0.1.2", features = ["parallel", "simd"] }

Available Features:

• parallel: Multi-threaded processing for large datasets
• simd: SIMD acceleration for numerical operations
• caching: Advanced caching for repeated computations

Usage

Basic usage examples:

use scirs2_series::{utils, decomposition, forecasting, features};
use scirs2_core::error::CoreResult;
use ndarray::array;

// Create a simple time series
fn time_series_example() -> CoreResult<()> {
    // Sample time series data
    let data = array![10.0, 11.0, 12.0, 11.5, 11.0, 10.5, 11.2, 12.5, 13.0, 12.7, 
                      12.0, 11.8, 12.2, 13.5, 14.0, 13.5, 13.0, 12.5, 13.0, 14.5];
    
    // Autocorrelation
    let acf = utils::autocorrelation(&data, 5)?;
    println!("Autocorrelation: {:?}", acf);
    
    // Partial autocorrelation
    let pacf = utils::partial_autocorrelation(&data, 5)?;
    println!("Partial autocorrelation: {:?}", pacf);
    
    // Decompose time series
    let decomposition = decomposition::seasonal_decompose(&data, 4, None, None)?;
    
    println!("Trend: {:?}", decomposition.trend);
    println!("Seasonal: {:?}", decomposition.seasonal);
    println!("Residual: {:?}", decomposition.resid);
    
    // Extract features
    let mean = features::mean(&data)?;
    let std_dev = features::standard_deviation(&data)?;
    let min = features::minimum(&data)?;
    let max = features::maximum(&data)?;
    
    println!("Time series features:");
    println!("Mean: {}", mean);
    println!("Standard deviation: {}", std_dev);
    println!("Min: {}", min);
    println!("Max: {}", max);
    
    // Forecast future values (simple moving average)
    let forecast = forecasting::moving_average_forecast(&data, 3, 5)?;
    println!("Forecast (next 5 points): {:?}", forecast);
    
    Ok(())
}

Components

Time Series Utilities

Functions for time series analysis:

use scirs2_series::utils::{
    autocorrelation,        // Calculate autocorrelation function
    partial_autocorrelation, // Calculate partial autocorrelation function
    cross_correlation,      // Calculate cross-correlation between two series
    lag_plot,               // Create lag plot data
    seasonal_plot,          // Create seasonal plot data
    difference,             // Difference a time series
    seasonal_difference,    // Apply seasonal differencing
    inverse_difference,     // Invert differencing
    lag_series,             // Create lagged versions of a time series
};

Decomposition

Methods for time series decomposition:

use scirs2_series::decomposition::{
    seasonal_decompose,     // Seasonal decomposition (additive or multiplicative)
    stl_decompose,          // STL decomposition (Seasonal-Trend decomposition using LOESS)
    hp_filter,              // Hodrick-Prescott filter
};

Forecasting

Time series forecasting methods:

use scirs2_series::forecasting::{
    moving_average_forecast, // Moving average forecast
    exponential_smoothing,  // Simple exponential smoothing
    double_exponential_smoothing, // Double exponential smoothing (Holt's method)
    triple_exponential_smoothing, // Triple exponential smoothing (Holt-Winters method)
    arima_forecast,         // ARIMA forecast
    sarima_forecast,        // Seasonal ARIMA forecast
};

Feature Extraction

Functions for extracting features from time series:

use scirs2_series::features::{
    // Basic Statistics
    mean,                   // Calculate mean
    standard_deviation,     // Calculate standard deviation
    minimum,                // Find minimum value
    maximum,                // Find maximum value
    
    // Trend Features
    trend_strength,         // Calculate trend strength
    seasonality_strength,   // Calculate seasonality strength
    
    // Complexity Measures
    entropy,                // Calculate entropy
    approximate_entropy,    // Calculate approximate entropy
    sample_entropy,         // Calculate sample entropy
    
    // Spectral Features
    spectral_entropy,       // Calculate spectral entropy
    dominant_frequency,     // Find dominant frequency
    
    // Other Features
    turning_points,         // Count turning points
    crossing_points,        // Count crossing points
    autocorrelation_features, // Extract autocorrelation features
};

Advanced Features

STL Decomposition

Seasonal-Trend decomposition using LOESS (STL):

use scirs2_series::decomposition::stl_decompose;
use ndarray::Array1;

// Sample time series
let data = Array1::from_vec(vec![/* time series data */]);

// STL decomposition parameters
let period = 12; // For monthly data
let robust = true;
let seasonal_degree = 1;
let seasonal_jump = 1;
let seasonal_window = 13;
let trend_degree = 1;
let trend_jump = 1;
let trend_window = 21;
let inner_iter = 2;
let outer_iter = 1;

// Perform STL decomposition
let decomposition = stl_decompose(&data, period, robust, 
                                 seasonal_degree, seasonal_jump, seasonal_window,
                                 trend_degree, trend_jump, trend_window,
                                 inner_iter, outer_iter).unwrap();

println!("Trend component: {:?}", decomposition.trend);
println!("Seasonal component: {:?}", decomposition.seasonal);
println!("Residual component: {:?}", decomposition.resid);

ARIMA Forecasting

Autoregressive Integrated Moving Average (ARIMA) model:

use scirs2_series::forecasting::arima_forecast;
use ndarray::Array1;

// Sample time series
let data = Array1::from_vec(vec![/* time series data */]);

// ARIMA parameters
let p = 1; // AR order
let d = 1; // Differencing order
let q = 1; // MA order

// Forecast horizon
let steps = 10;

// Perform ARIMA forecast
let (forecast, conf_intervals) = arima_forecast(&data, p, d, q, steps, 0.95).unwrap();

println!("ARIMA({},{},{}) forecast: {:?}", p, d, q, forecast);
println!("95% confidence intervals: {:?}", conf_intervals);

Contributing

See the CONTRIBUTING.md file for contribution guidelines.

License

This project is dual-licensed under:

• MIT License
• Apache License Version 2.0

You can choose to use either license. See the LICENSE file for details.