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 | Conference PDF | Archive PDF | Plain Text | 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. |