# SciRS2 Series
[](https://crates.io/crates/scirs2-series)
[[]](../LICENSE)
[](https://docs.rs/scirs2-series)
**Production-ready** time series analysis module for the SciRS2 scientific computing library (v0.1.0-rc.2). Following the [SciRS2 POLICY](../SCIRS2_POLICY.md), this **Release Candidate 2** 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
**Release Candidate 2 - Production Ready**
Add the following to your `Cargo.toml`:
```toml
[dependencies]
scirs2-series = "0.1.0-rc.2"
```
**Recommended for Production**: Enable performance optimizations:
```toml
[dependencies]
scirs2-series = { version = "0.1.0-rc.2", features = ["parallel", "simd"] }
scirs2-core = { version = "0.1.0-rc.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:
```rust
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:
```rust
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:
```rust
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:
```rust
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:
```rust
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):
```rust
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:
```rust
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](../CONTRIBUTING.md) file for contribution guidelines.
## License
This project is dual-licensed under:
- [MIT License](../LICENSE-MIT)
- [Apache License Version 2.0](../LICENSE-APACHE)
You can choose to use either license. See the [LICENSE](../LICENSE) file for details.