anomaly-impact-alert 0.2.0

Anomaly detection, impact explanation, forecasting, and alerting toolkit.

anomaly_impact_alert

Anomaly Impact Alert — a lightweight Python toolkit for detecting anomalies, forecasting metrics, explaining deviations, and sending alerts (e.g. via Telegram).

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Purpose

This library helps analytics and monitoring teams automatically:

• Detect abnormal metric values in time-series data
• Forecast expected ranges (with confidence intervals)
• Explain the drivers behind changes (countries, platforms, etc.)
• Send structured alerts to Telegram

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Example Output

Detection visualization

Shows detected anomalies (red dots), confidence intervals, and model outputs:

Telegram alert

Example of an automatically formatted alert with top contributing segments:

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Quick Start

pip install anomaly_impact_alert

from anomaly_impact_alert import (
    AnomalyParams, analyze_latest_point,
    ImpactConfig, attach_impact_text,
    BFConfig, forecast_values_for_targets_better,
    AlertConfig, send_alert_for_date,
)

# 1. Detect anomalies
params = AnomalyParams(threshold=3.0)
anomalies = analyze_latest_point(df, params)

# 2. Forecast expected values
forecast_cfg = BFConfig(periods=7)
forecast = forecast_values_for_targets_better(df, forecast_cfg)

# 3. Explain the impact
impact_cfg = ImpactConfig(metric="amount")
text = attach_impact_text(anomalies, impact_cfg)

# 4. Send alert to Telegram
alert_cfg = AlertConfig(bot_token="YOUR_BOT_TOKEN", chat_id="YOUR_CHAT_ID")
send_alert_for_date(text, alert_cfg)

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Modules Overview

Module	Description
anomaly_detector	Detects anomalies using statistical and ML methods
forecast	Forecasts values and confidence intervals
impact_explainer	Explains which dimensions cause deviations
alert_bot_telegram	Sends rich alerts to Telegram

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Dependencies

pandas, numpy, scikit-learn, matplotlib, prophet, tqdm, requests, python-telegram-bot

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License

MIT © 2025 Aleksey Voronko, Petr Devitsin