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Anomaly-detection

Cobblestone assignment

Efficient Data Stream Anomaly Detection

Description

A Python-based project to detect anomalies in real-time data streams, simulating patterns and seasonal variations.

Requirements

• Python 3.x
• NumPy
• Matplotlib

Installation

1. Clone the repository.
2. Install dependencies:

   pip install -r requirements.txt

efficient Data Stream Anomaly Detection

Overview

This project implements a real-time anomaly detection system for data streams using a rolling Z-score algorithm. It identifies unusual data points in streaming data, such as revenue, and visualizes them in real-time. The system is highly configurable and can process data from simulations or real-world datasets in CSV format.

Features

Real-Time Detection: Identifies anomalies in a data stream using a rolling Z-score algorithm.

Customizable Parameters: Users can adjust the rolling window size and Z-score threshold for optimal performance. Real-Time Visualization: Visualizes data streams with anomalies marked in real-time. CSV Support: Processes real-world data from CSV files, supporting single or multiple columns. Error Handling: Robust error handling and validation for input data. Unit Tests: Ensures reliability with tests for anomaly detection and data processing. Algorithm Explanation The project uses a Rolling Z-Score algorithm to detect anomalies:

A rolling window tracks the most recent data points. The mean and standard deviation of the rolling window are computed. A data point is flagged as an anomaly if it deviates from the mean by more than a user-defined threshold (e.g., 3 standard deviations). This algorithm is lightweight, efficient, and suitable for real-time data streams.

Installation Prerequisites Python 3.x (tested with Python 3.10+) Minimal external libraries (see requirements.txt).

Install Dependencies

Run the following command to install required libraries:

pip install -r requirements.txt

code run

python src/main.py --file data/clean_data.csv --column revenue --window_size 50 --threshold 3.0

328a5b3 (Initial commit)