anomx 0.2.2

Installable time-series anomaly library with forecasting, reconstruction, and representation base approaches.

anomx

anomx is the core Python library for the Anomx platform: time-series datasets, forecasting models, anomaly scorers, and anomaly detectors built for interpretable, production-oriented workflows.

Anomx is a data intelligence platform focused on anomaly detection and predictive insights in complex, data-driven systems. The platform handles orchestration, workers, storage, connectors, audit trails, and human-in-the-loop feedback. This repository contains the reusable modeling layer that those systems compose.

Goals

• Provide clean primitives for time-series datasets, scorers, detectors, and models.
• Support both batch and online anomaly detection workflows.
• Keep outputs interpretable: scores, thresholds, timestamps, labels, and metadata.
• Stay modular enough to power the Anomx platform without coupling the library to the platform's storage or orchestration layer.
• Integrate with the broader time-series ecosystem, especially Darts, through optional adapters.

Installation

The package is structured for PyPI distribution:

pip install anomx

For local development:

git clone https://github.com/anomx/anomx.git
cd anomx
python -m pip install -e ".[dev]"

Optional extras:

pip install "anomx[darts]"     # Darts forecasting model adapters
pip install "anomx[docs]"      # Documentation tooling
pip install "anomx[platform]"  # Platform-facing IO dependencies
pip install "anomx[release]"   # Build and PyPI publishing tooling

Package Layout

src/anomx/
  datasets/      TimeSeriesDataset, metadata, dataset loaders, transforms
  scorers/       Anomaly score contracts and scoring implementations
  detectors/     Batch and online anomaly detector contracts and implementations
  models/        Forecasting model contracts and baseline forecasters
  integrations/  Optional adapters, including Darts

Quick Start

from anomx.datasets import make_sine_anomaly_dataset
from anomx.detectors import MovingAverageDetector
from anomx.models import NaiveSeasonalModel

dataset = make_sine_anomaly_dataset()

detector = MovingAverageDetector(window=24, threshold=3.0)
result = detector.fit_predict(dataset)
print(result.to_dataframe().tail())

model = NaiveSeasonalModel(season_length=24).fit(dataset)
forecast = model.predict(12)
print(forecast.to_dataframe())

Darts Integration

The default install stays lightweight. To use Darts models, install the optional extra and wrap any compatible Darts forecasting model:

from darts.models import ExponentialSmoothing

from anomx.datasets import make_sine_anomaly_dataset
from anomx.integrations import DartsForecastingModel

dataset = make_sine_anomaly_dataset()
model = DartsForecastingModel(ExponentialSmoothing())
forecast = model.fit(dataset).predict(24)

The intent is to let Anomx expose Darts-backed forecasting and anomaly workflows without making Darts a required dependency for every user.

Development

python -m pip install -e ".[dev]"
pytest
ruff check .
mypy src/anomx
python -m pip install -e ".[release]"
python -m build

Platform Context

Anomx prioritizes:

• Signal over noise: surface only relevant insights.
• Clarity over complexity: outputs should be interpretable.
• Actionability: every insight should support a decision.

The platform is designed around modular pipelines, real-time and batch workers, heterogeneous data sources, and versioned entities such as datasets, datasources, channels, jobs, runs, findings, and model artifacts. This library is the focused modeling and time-series package used by that larger system.

Status

This repository is in pre-alpha scaffold stage. The public API should be treated as provisional until the first stable release.