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..
MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series
Authors: Jufang Duan, Wei Zheng, Yangzhou Du, Wenfa Wu, Haipeng Jiang, Hongsheng Qi
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the effectiveness of MF-CLR, we conduct extensive experiments on five downstream tasks, including long-term and short-term forecasting, classification, anomaly detection and imputation. Experimental evidence shows that MF-CLR delivers a leading performance in all the downstream tasks and keeps consistent performance across different target dataset scales in the transfer learning scenario. |
| Researcher Affiliation | Industry | Jufang Duan 1 Wei Zheng 1 Yangzhou Du 1 Wenfa Wu 1 Haipeng Jiang 1 Hongsheng Qi 1 1Lenovo Research, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 Hierarchical training. |
| Open Source Code | Yes | The source code is publicly available at https://github.com/duanjufang/MF-CLR. |
| Open Datasets | Yes | To balance between the general acceptance and suitability, we resample these public datasets by different frequencies along the feature dimension to meet the multi-frequency setting. Details about the public datasets preprocessing can be found in Appendix B. |
| Dataset Splits | Yes | The datasets are divided into training (70%), validation (10%) and test (20%) unless the given dataset has already been divided. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models, specific cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper describes the model architecture and parameters but does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | For all the downstream tasks, we set the batch size to be 32, the initial learning rate to be 1E-3 with 70% decay every 10 steps, and α to be 1E-5. The encoder of MF-CLR contains 4 hidden dilated convolutional layers with feed-forward connections between consecutive blocks. The dilation is set to be 2i for the i-th layer with the hidden channel size of 64 and the kernel size of 3. Also, a 10% dropout is added to enhance robustness. |