DR4MTSAD (Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series) 🚀

Overview 👀

This repository contains the implementation and analysis of two advanced unsupervised anomaly detection models - MUTANT and Anomaly-Transformer - applied to time series data. The focus of this project is on integrating dimensionality reduction techniques to enhance the performance and efficiency of these models.

Results 🔥

Results: Extensive Performance Comparison of MUTANT and Anomaly-Transformer Models Under Various Dimensionality Reduction Techniques Across MSL, SMAP and SWaT Datasets

Model	# Dimensions Remaining	DR Layer (Technique)	MSL			SMAP			SWaT
			Precision	Recall	F1-score	Precision	Recall	F1-score	Precision	Recall	F1-score
MUTANT	(Original)	None	0.9464	0.9520	0.9492	0.9658	0.9787	0.9722	0.9805	0.9881	0.9842
	To Half Dim. 27 - 12 - 25	PCA	0.9307	0.9807	0.9551	0.9725	0.9630	0.9678	0.9729	1.0000	0.9863
		Rand. Proj.	0.8619	1.0000	0.9258	0.9703	0.9782	0.9742	0.9782	0.9882	0.9832
		UMAP	0.8846	0.9762	0.9281	0.9836	0.9453	0.9640	0.9491	0.9838	0.9661
	To Lowest Dim. 8 - 8 - 8	PCA	0.9184	0.9848	0.9505	0.9882	0.9659	0.9769	0.9632	0.9866	0.9748
		Rand. Proj.	0.9331	0.9762	0.9542	0.9550	0.9866	0.9706	0.9728	0.9856	0.9792
		UMAP	0.9341	0.9914	0.9619	0.9913	0.9399	0.9649	0.9833	0.9788	0.9810
Anomaly-Transformer	(Original)	None	0.9188	0.9473	0.9329	0.9381	0.9939	0.9652	0.8844	1.0000	0.9386
	To Half Dim. 27 - 12 - 25	PCA	0.9146	0.9436	0.9289	0.9111	0.9916	0.9497	0.9223	1.0000	0.9596
		Rand. Proj.	0.9191	0.9773	0.9473	0.9160	0.9950	0.9539	0.8889	1.0000	0.9412
		UMAP	0.9178	0.9735	0.9448	0.9264	0.9993	0.9615	0.8482	1.0000	0.9179
	3 - 3 - 3	PCA	0.9172	0.9676	0.9417	0.9072	0.9945	0.9489	0.9706	0.9495	0.9600
		Rand. Proj.	0.9180	0.9793	0.9477	0.9335	0.9919	0.9618	0.9891	0.8619	0.9212
		UMAP	0.9171	0.9560	0.9361	0.9320	0.9915	0.9608	0.9807	0.9229	0.9509
		t-SNE	0.9164	0.9490	0.9324	0.9310	0.9962	0.9625	0.9843	0.9082	0.9447
	To Lowest Dim. 2 - 2 - 2	PCA	0.9180	0.9683	0.9425	0.9070	0.9930	0.9481	0.9492	0.9696	0.9593
		Rand. Proj.	0.9210	0.9473	0.9340	0.9429	0.9524	0.9476	0.9876	0.8862	0.9341
		UMAP	0.9183	0.9677	0.9423	0.9330	0.9945	0.9628	0.9890	0.8871	0.9352
		t-SNE	0.9197	0.9749	0.9465	0.9353	0.9977	0.9655	0.9854	0.9237	0.9536

Models 💾

• MUTANT: Leveraging Graph Convolutional Networks (GCNs) and attention-based Variational Auto-Encoders (VAEs).
• Anomaly-Transformer: Utilizing association discrepancies for anomaly identification.

Dimensionality Reduction Techniques 🔄

• PCA (Principal Component Analysis)
• UMAP (Uniform Manifold Approximation and Projection)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• Random Projection

Notebooks 📓

• Anomaly_Transformer.ipynb: Training and testing the Anomaly-Transformer model.
• MUTANT.ipynb: Training and testing the MUTANT model.
• ApplyDimensionalityReduction.ipynb: Application of various dimensionality reduction techniques to the datasets.

Data 📊

The data directory contains the following datasets used for empirical analysis:

• MSL: Mars Science Laboratory Rover dataset.
• SMAP: Soil Moisture Active Passive Satellite dataset.
• SWaT: Secure Water Treatment dataset.

Results 📈

• Results.md: A detailed markdown file containing the results and findings of the model training and testing.

How to Use 🔧

Each Jupyter Notebook is self-contained and includes the necessary code for model training, testing, and applying dimensionality reduction techniques. Follow the steps in each notebook to replicate the experiments.

Important Note 🗒️

While running the Anomaly-Transformer model, please change the file Anomaly-Transformer/data_factory/data_loader.py.

Key Findings 💡

• Significant enhancement in anomaly detection performance with dimensionality reduction.
• Notable reduction in training times, especially when data is reduced to its lowest dimensions.
• Both MUTANT and Anomaly-Transformer models show adaptability and robustness across various datasets and dimensionality reduction scenarios.

Contributing 🤝

Contributions to this project are welcome! Please refer to the issues tab for pending enhancements and bug fixes.

Citation 📖

If you use this work in your research, please cite:

"Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series"

Related Repos 📂

https://github.com/mahsunaltin/3DMesh

License 📄

This project is licensed under the terms of the MIT license.