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📜 Time-Series Anomaly Detection

🎯 AIM

To detect anomalies in time-series data using Long Short-Term Memory (LSTM) networks.

[NOT USED]

📓 KAGGLE NOTEBOOK

https://www.kaggle.com/code/thatarguy/lstm-anamoly-detection/notebook

Kaggle Notebook

⚙️ TECH STACK

Category Technologies
Languages Python
Libraries/Frameworks TensorFlow, Keras, scikit-learn, numpy, pandas, matplotlib
Tools Jupyter Notebook, VS Code

📝 DESCRIPTION

What is the requirement of the project?

  • The project focuses on identifying anomalies in time-series data using an LSTM autoencoder. The model learns normal patterns and detects deviations indicating anomalies.
Why is it necessary?
  • Anomaly detection is crucial in various domains such as finance, healthcare, and cybersecurity, where detecting unexpected behavior can prevent failures, fraud, or security breaches.
How is it beneficial and used?
  • Businesses can use it to detect irregularities in stock market trends.
  • It can help monitor industrial equipment to identify faults before failures occur.
  • It can be applied in fraud detection for financial transactions.
How did you start approaching this project? (Initial thoughts and planning)
  • Understanding time-series anomaly detection methodologies.
  • Generating synthetic data to simulate real-world scenarios.
  • Implementing an LSTM autoencoder to learn normal patterns and detect anomalies.
  • Evaluating model performance using Mean Squared Error (MSE).
Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.).
  • Research paper: "Deep Learning for Time-Series Anomaly Detection"
  • Public notebook: LSTM Autoencoder for Anomaly Detection

🔍 PROJECT EXPLANATION

🧩 DATASET OVERVIEW & FEATURE DETAILS

📂 Synthetic dataset
  • The dataset consists of a sine wave with added noise.
Feature Name Description Datatype
time Timestamp int64
value Sine wave value with noise float64

🛤 PROJECT WORKFLOW

Project workflow

  • Generate synthetic data (sine wave with noise)
  • Normalize data using MinMaxScaler
  • Split data into training and validation sets
  • Create sequential data using a rolling window approach
  • Reshape data for LSTM compatibility
  • Implement LSTM autoencoder for anomaly detection
  • Optimize model using Adam optimizer
  • Compute reconstruction error for anomaly detection
  • Identify threshold for anomalies using percentile-based method
  • Visualize detected anomalies using Matplotlib

🖥 CODE EXPLANATION

  • The model consists of an encoder, bottleneck, and decoder.
  • It learns normal time-series behavior and reconstructs it.
  • Deviations from normal patterns are considered anomalies.

⚖️ PROJECT TRADE-OFFS AND SOLUTIONS

  • Setting a high threshold may miss subtle anomalies, while a low threshold might increase false positives.
  • Solution: Use the 95th percentile of reconstruction errors as the threshold to balance false positives and false negatives.

🖼 SCREENSHOTS

Visualizations and EDA of different features

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Model performance graphs

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📉 MODELS USED AND THEIR EVALUATION METRICS

Model Reconstruction Error (MSE)
LSTM Autoencoder 0.015

✅ CONCLUSION

🔑 KEY LEARNINGS

Insights gained from the data

  • Time-series anomalies often appear as sudden deviations from normal patterns.
Improvements in understanding machine learning concepts
  • Learned about LSTM autoencoders and their ability to reconstruct normal sequences.
Challenges faced and how they were overcome
  • Handling high reconstruction errors by tuning model hyperparameters.
  • Selecting an appropriate anomaly threshold using statistical methods.

🌍 USE CASES

  • Detect irregular transaction patterns using anomaly detection.
  • Identify equipment failures in industrial settings before they occur.