avila-telemetry

Crates.ioavila-telemetry
lib.rsavila-telemetry
version0.1.0
created_at2025-11-22 21:03:10.964344+00
updated_at2025-11-22 21:03:10.964344+00
descriptionArxis Observatory: Monitoring the AXIS (engine) - Time series, ARIMA, anomaly detection, forecasting, NASA-grade data quality
homepagehttps://arxis.avilaops.com
repositoryhttps://github.com/avilaops/arxis
max_upload_size
id1945736
size134,022
Nícolas Ávila (avilaops)

documentation

https://docs.rs/avila-telemetry

README

avila-telemetry

⚙️ The Observatory of Arxis

Time series analysis and observability - Monitoring the engine's heartbeat

License: MIT Rust Part of Arxis

avila-telemetry is the observatory within the citadel - monitoring the AXIS (engine) to ensure optimal performance.

Like a watchful sentinel, it detects anomalies, forecasts trends, and ensures NASA-grade data quality for scientific missions.

Features

Time Series Analysis

  • Operations: Moving average, exponential smoothing, differencing
  • Statistics: Mean, std dev, min, max, percentiles
  • Transformations: Slicing, resampling, windowing

Anomaly Detection

  • Statistical Methods: Z-score (configurable threshold)
  • Robust Methods: IQR (Interquartile Range) detection
  • Use Cases: Glitch detection, instrumental artifacts, outliers

Forecasting

  • ARIMA: AutoRegressive Integrated Moving Average
  • Exponential Smoothing: Simple, double, triple
  • Applications: Trend prediction, observation planning

Data Quality (NASA Standards)

  • Quality Metrics: Accuracy, completeness, consistency, validity
  • Scoring: Overall quality score (0-1), NASA threshold (≥0.95)
  • Observability: Structured logging, alerts, performance tracking

Usage

use avila_telemetry::{TimeSeries, AnomalyDetector};

// Time series operations
let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.0, 2.0];
let ts = TimeSeries::new(data);
let ma = ts.moving_average(3)?;
let stats = ts.statistics();

// Anomaly detection
let detector = AnomalyDetector::new(3.0, 1.5); // 3-sigma, 1.5 IQR
let anomalies = detector.detect_zscore(&ts)?;
println!("Found {} anomalies", anomalies.len());

// Forecasting
use avila_telemetry::forecasting::ExponentialSmoothing;
let forecaster = ExponentialSmoothing::simple(0.3);
let forecast = forecaster.predict(&ts, 5)?; // 5 steps ahead

Installation

[dependencies]
avila-telemetry = { git = "https://github.com/avilaops/arxis", branch = "main" }
chrono = "0.4"

Tests

cargo test -p avila-telemetry

⚙️ Part of Arxis

avila-telemetry is the observatory of Arxis - monitoring the engine.

ARX (fortress) + AXIS (engine) = ARXIS

Built with ❤️ by Avila


## Installation

```toml
[dependencies]
avila-telemetry = { git = "https://github.com/avilaops/arxis", branch = "main" }
chrono = { version = "0.4", features = ["serde"] }

Examples

cargo run --example basic_operations
cargo run --example anomaly_detection
cargo run --example forecasting
cargo run --example nasa_gcp_observability

Tests

cargo test -p avila-telemetry

22 tests passing

License

MIT - See LICENSE for details

Commit count: 0

cargo fmt