MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
Authors: Junho Song, Keonwoo Kim, Jeonglyul Oh, Sungzoon Cho
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed method on five real-world datasets from diverse domains, and it achieves an average anomaly detection F1-score of 95.74%, significantly outperforming the previous state-of-the-art methods. We also conduct extensive experiments to empirically validate the effectiveness of our proposed model s key components. |
| Researcher Affiliation | Collaboration | Junho Song1 Keonwoo Kim1,2 Jeonglyul Oh1 Sungzoon Cho1 1Seoul National University 2VRCREW Inc. |
| Pseudocode | Yes | Algorithm 1 Memory module initialization with K-means clustering. Algorithm 2 Proposed Method MEMTO. |
| Open Source Code | No | The paper does not explicitly state that the source code for MEMTO is publicly available or provide a link to it. |
| Open Datasets | Yes | We evaluate MEMTO on five real-world multivariate time series datasets. (i) Server Machine Dataset (SMD [33])... (ii & iii) Mars Science Laboratory rover (MSL) and Soil Moisture Active Passive satellite (SMAP) are public data released from NASA [13]... (iv) Secure Water Treatment (SWa T [18])... (v) Pooled Server Metrics (PSM [1])... We obtained SWa T by submitting a request through https://itrust.sutd.edu.sg/itrust-labs_datasets/. |
| Dataset Splits | Yes | We split the training data into 80% for training and 20% for validation. |
| Hardware Specification | Yes | Our experiments are conducted using the Pytorch framework on four NVIDIA GTX 1080 Ti 12GB GPUs. |
| Software Dependencies | No | Our experiments are conducted using the Pytorch framework on four NVIDIA GTX 1080 Ti 12GB GPUs. |
| Experiment Setup | Yes | We set λ in the objective function to 0.01, use Adam optimizer [15] with a learning rate of 5e-5, and employ early stopping with the patience of 10 epochs against the validation loss during training. Our experiments are conducted using the Pytorch framework on four NVIDIA GTX 1080 Ti 12GB GPUs. Furthermore, during the execution of our experiment, we make partial references to the code of [40]. We performed a grid search to determine the values of each hyperparameter within the following range: λ {1e+0, 5e-1, 1e-1, 5e-2, 1e-2, 5e-3, 1e-3} lr {1e-4, 3e-4, 5e-4, 1e-5, 3e-5, 5e-5} τ {0.1, 0.3, 0.5, 0.7, 0.9} M {5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100}. |