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..
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
Authors: Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To fully evaluate Sim MTM, we conduct experiments on two typical time series analysis tasks: forecasting and classification, covering low-level and high-level representation learning. Further, we present the fine-tuning performance for each task under inand cross-domain settings. |
| Researcher Affiliation | Academia | Jiaxiang Dong , Haixu Wu , Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long B School of Software, BNRist, Tsinghua University, China EMAIL, EMAIL EMAIL |
| Pseudocode | No | The paper describes the Sim MTM framework with equations and textual descriptions but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/thuml/Sim MTM. |
| Open Datasets | Yes | Table 1: Summary of experiment benchmarks. Tasks Datasets Semantic Forecasting ETTh1,ETTh2 Electricity ETTm1,ETTm2 Electricity Weather Weather Electricity Electricity Traffic Transportation Classification Sleep EEG EEG Epilepsy EEG FD-B Faulty Detection Gesture Hand Movement EMG Muscle Responses. A.1 Dataset Description: (1) ETT (4 subsets) [58], (2) WEATHER [43], (3) ELECTRICITY [38], (4) TRAFFIC [29], (5) SLEEPEEG [16], (6) EPILEPSY [1], (7) FD-B [19], (8) GESTURE [22], (9) EMG [30]. |
| Dataset Splits | Yes | Table 7: Dataset descriptions. Samples are organized in (Train/Validation/Test). |
| Hardware Specification | Yes | All the experiments are repeated five times, implemented in Py Torch [28] and conducted on NVIDIA A100 SXM4 40GB GPU. |
| Software Dependencies | No | The paper states 'implemented in Py Torch [28]' but does not provide a specific version number for PyTorch or any other software dependencies beyond the general library name. |
| Experiment Setup | Yes | Table 10: Model and training configuration in Forecasting (Fore.) and Classification (Class.) tasks. Encoder Pre-training Fine-tuning elayers dmodel learning rate batch size epochs learning rate loss function batch size epochs Fore. 2 16 1e-3 32 50 1e-4 L2 {16,32} 10 Class. 3 128 1e-4 128 10 1e-4 Cross-Entropy 32 300 |