https://github.com/deepmancer/timeseries-anomaly-detection

Analysis of Classical Machine Learning Algorithms for Anomaly Detection in Time Series Data
https://github.com/deepmancer/timeseries-anomaly-detection

autoencoder isolation-forest local-outlier-factor machine-learning-algorithms python rolling-statistics scikit-learn scikitlearn-machine-learning step-by-step step-by-step-guide time-series time-series-anomaly-detection z-score

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Analysis of Classical Machine Learning Algorithms for Anomaly Detection in Time Series Data

• Host: GitHub
• URL: https://github.com/deepmancer/timeseries-anomaly-detection
• Owner: deepmancer
• Created: 2022-09-17T08:47:53.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-16T13:23:49.000Z (4 months ago)
• Last Synced: 2024-10-11T20:02:03.485Z (3 months ago)
• Topics: autoencoder, isolation-forest, local-outlier-factor, machine-learning-algorithms, python, rolling-statistics, scikit-learn, scikitlearn-machine-learning, step-by-step, step-by-step-guide, time-series, time-series-anomaly-detection, z-score
• Language: Jupyter Notebook
• Homepage:
• Size: 406 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 📈 Time Series Classic Anomaly Detection



  

  

  



---

## 📖 Overview

This repository provides an in-depth exploration of time series anomaly detection techniques, utilizing classic machine learning models. The project is implemented in a Jupyter Notebook, which offers an interactive environment to experiment with and understand the methods used for detecting anomalies in time series data.

---

## 📝 Project Description

Time series anomaly detection is a critical task in various domains, from finance to IoT, where identifying unexpected behavior in data is essential for decision-making and security. This project explores classic anomaly detection techniques tailored for time series data, including methods like:

- **Z-Score Analysis**: Detects anomalies based on statistical deviation.

- **Rolling Statistics**: Uses moving averages and standard deviations to flag anomalies.

- **Isolation Forest**: A tree-based model that isolates anomalies in high-dimensional data.

- **Local Outlier Factor (LOF)**: Identifies anomalies based on the local density of data points.

- **Autoencoders**: Neural networks trained to reconstruct input data, with anomalies detected as poorly reconstructed instances.

### Key Features:

- **Step-by-Step Implementation**: Each method is implemented from scratch, with explanations provided for the underlying concepts.

- **Interactive Exploration**: The Jupyter Notebook format allows for interactive experimentation, making it easy to adjust parameters and observe the effects on anomaly detection.

- **Visualizations**: Detailed plots and graphs are included to visualize the data and highlight detected anomalies.

---

## 🛠️ Prerequisites

Ensure that you have the following dependencies installed to run the Jupyter Notebook:

- **Python 3.6+**

- **Jupyter Notebook**

- **Pandas**

- **NumPy**

- **Scikit-learn**

- **Matplotlib**

- **Seaborn**

You can install the required libraries via pip:

```bash

pip install jupyter pandas numpy scikit-learn matplotlib seaborn

```