Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
Authors: Chen Liu, Shibo He, Qihang Zhou, Shizhong Li, Wenchao Meng
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are conducted to demonstrate the superiority of our method on 9 univariate datasets and 6 multivariate datasets. and 4 Experiment |
| Researcher Affiliation | Academia | Chen Liu1 , Shibo He1 , Qihang Zhou1 , Shizhong Li1 and Wenchao Meng1 1Zhejiang University EMAIL |
| Pseudocode | No | The paper describes the methodology using prose and a framework diagram (Figure 2), but it does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | UCR Anomaly Archive (UCR). This archive comprises 250 diverse univariate time series signals spanning various domains [Wu and Keogh, 2021]. and We also evaluate our method on 6 previously commonly used multivariate datasets, including SMD, MSL, SMAP, PSM, Wa Q, and SWAN. |
| Dataset Splits | No | Tables 1 and 2 list "Train" and "Test" data, but there is no explicit mention or details of a "validation" split in the text or tables. |
| Hardware Specification | Yes | All experiments are conducted on a single RTX 3090. |
| Software Dependencies | No | The paper mentions software components like "Adam optimizer" and "GPT2" but does not specify their version numbers or other ancillary software dependencies. |
| Experiment Setup | Yes | During the training stage, we utilize an Adam optimizer with a learning rate of 0.0001 and a batch size of 32. All experiments are conducted on a single RTX 3090. and For our model, we use the pretrained GPT2 with 6 layers as our teacher network. Regarding the student network, we use a pool with 32 prototypes and an attention mechanism with an intermediate dimension of 64 and a head number of 8. |