Crate avila_telemetry 

avila-telemetry

Time series analysis, telemetry, and forecasting library for Rust.

Features

• Time Series Analysis: ARIMA, SARIMA, State Space Models
• Anomaly Detection: Statistical and ML-based detection
• Forecasting: Multi-step prediction with probabilistic forecasting
• Feature Engineering: Lag features, rolling statistics, seasonality decomposition

Example

use avila_telemetry::TimeSeries;

// Create a time series
let data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
let ts = TimeSeries::new(data);

// Calculate moving average
let ma = ts.moving_average(3);

Re-exports

pub use anomaly::Anomaly;

pub use anomaly::AnomalyDetector;

pub use anomaly::AnomalyType;

pub use decomposition::Decomposer;

pub use decomposition::DecompositionResult;

pub use decomposition::DecompositionType;

pub use features::FeatureExtractor;

pub use forecasting::ExponentialSmoothing;

pub use forecasting::ForecastResult;

pub use forecasting::Forecaster;

pub use forecasting::MovingAverageForecaster;

pub use observability::Alert;

pub use observability::AlertLevel;

pub use observability::DataQuality;

pub use observability::DataQualityAssessment;

pub use observability::ErrorBudget;

pub use observability::GoldenSignals;

pub use observability::LogSeverity;

pub use observability::NASAQualityControl;

pub use observability::PerformanceTracker;

pub use observability::ServiceLevelObjective;

pub use observability::StructuredLog;

pub use time_series::TimeSeries;

Modules

anomaly

Anomaly detection algorithms

decomposition

Time series decomposition (trend, seasonality, residuals)

features

Feature engineering for time series

forecasting

Forecasting models and algorithms

models

Statistical models for time series

observability

Observability module integrating NASA and Google Cloud best practices

time_series

Core time series data structure and operations

Enums

TelemetryError

Common error type for the library

Type Aliases

Result