libspot-rs

A pure Rust implementation of the SPOT (Streaming Peaks Over Threshold) algorithm for real-time anomaly detection in time series data.

Quick Start

use libspot_rs::{SpotDetector, SpotConfig, SpotStatus};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create detector with default configuration
    let config = SpotConfig::default();
    let mut detector = SpotDetector::new(config)?;

    // Fit with training data
    let training_data: Vec<f64> = (0..1000)
        .map(|i| 5.0 + (i as f64 * 0.01).sin() * 2.0)
        .collect();
    detector.fit(&training_data)?;

    // Detect anomalies in real-time
    let test_value = 50.0; // This should be an anomaly
    match detector.step(test_value)? {
        SpotStatus::Normal => println!("Normal data point"),
        SpotStatus::Excess => println!("In the tail distribution"),
        SpotStatus::Anomaly => println!("Anomaly detected! 🚨"),
    }

    Ok(())
}

Features

Serialization (Model Persistence)

Serialization support is enabled by default. SPOT detectors can be serialized and deserialized for model deployment:

[dependencies]
libspot-rs = { version = "0.2.1" }  # serde is enabled by default
serde_json = "1.0"

To disable serialization support (e.g., for minimal dependencies), use:

[dependencies]
libspot-rs = { version = "0.2.1", default-features = false }

This enables:

Model persistence: Save trained models to disk and load them later
Model deployment: Export models for use in production systems
Model sharing: Share trained models between applications
Checkpointing: Save model state during long-running processes

Example usage:

use libspot_rs::{SpotConfig, SpotDetector};
use serde_json;

// Train a model
let config = SpotConfig::default();
let mut spot = SpotDetector::new(config).unwrap();
let training_data: Vec<f64> = (0..1000).map(|i| i as f64 / 100.0).collect();
spot.fit(&training_data).unwrap();

// Save the model to a JSON file
let json = serde_json::to_string_pretty(&spot).unwrap();
std::fs::write("model.json", &json).unwrap();

// Later, load the model and continue using it
let json = std::fs::read_to_string("model.json").unwrap();
let mut loaded: SpotDetector = serde_json::from_str(&json).unwrap();

// The loaded model is ready to use immediately
let status = loaded.step(50.0).unwrap();

The serialization handles special float values (NaN, Infinity) correctly, ensuring that models can be reliably saved and restored.

Alternative

For C FFI bindings to the original libspot library, see the libspot crate.

License