readme.md

LSTM Autoencoder for Time Series Anomaly Detection

This repository contains the implementation of an LSTM (Long Short-Term Memory) Autoencoder for detecting anomalies in time series data. You can either use a pre-trained model provided here or train a new model on your own CSV dataset.

An interactive demo is available on Hugging Face Spaces, and a Google Colab notebook is provided for experimentation.

Key Features

• LSTM Autoencoder: Built using PyTorch.
• Two Modes:
  1. Use Pre-trained Model: Quickly analyze time series data using the included model.
  2. Train on Custom Data: Upload your own CSV file to train a new LSTM Autoencoder tailored to your specific data.
• Comprehensive Output: Generates insightful plots and artifacts:
  • Andrews Curves Plot
  • Training Loss Curve
  • Anomaly Score Distribution
  • Evaluation Curve (e.g., ROC Curve, Precision-Recall Curve, or your custom "ANDRE" curve - please clarify if "ANDRE" is a custom metric)
• Downloadable Results: Packages the trained model, data scalers, and all generated plots into a convenient ZIP file for download.
• Interactive Demo: Hugging Face Space for easy interaction without local setup.
• Colab Notebook: Experiment with the code, training, and evaluation in a Google Colab environment.

How it Works

An LSTM Autoencoder is trained on 'normal' time series data.

1. The Encoder (an LSTM network) learns to compress the input time series into a lower-dimensional latent representation.
2. The Decoder (another LSTM network) learns to reconstruct the original time series from this latent representation.
3. During inference, the model tries to reconstruct new, unseen time series sequences.
4. If a sequence is similar to the normal data seen during training, the reconstruction error (the difference between the input and the reconstructed output) will be low.
5. If a sequence contains anomalies (patterns not seen during training), the model struggles to reconstruct it accurately, resulting in a high reconstruction error.
6. By setting a threshold on the reconstruction error, we can classify sequences as normal or anomalous.

Installation (Local Setup)

1. Clone the repository:

   git clone https://github.com/Rajatsingh24/LSTM-based-Autoencoder.git
   cd LSTM-based-Autoencoder
   

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install dependencies: Make sure you have a requirements.txt file in your repository.

   pip install -r requirements.txt
   

Usage

You can interact with the model primarily through the Hugging Face Space or the Colab Notebook.