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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching
Authors: Fernando Moreno-Pino, Alvaro Arroyo, Harrison Waldon, Xiaowen Dong, Alvaro Cartea
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 33,018 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,723 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present empirical results for the effectiveness of the Rough Transformer, hereafter denoted RFormer, on a variety of time-series-related tasks. |
| Researcher Affiliation | Academia | Fernando Moreno-Pino1, Álvaro Arroyo1, Harrison Waldon1, Xiaowen Dong1,2 Álvaro Cartea1,3 1 Oxford-Man Institute, University of Oxford 2 Machine Learning Research Group, University of Oxford 3 Mathematical Institute, University of Oxford |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at: https://github.com/AlvaroArroyo/RFormer |
| Open Datasets | Yes | Next, we consider the Heart Rate dataset from the TSR archive [83], originally sourced from Beth Israel Deaconess Medical Center (BIDMC). |
| Dataset Splits | Yes | As previously done in [57], the original train and test datasets are merged and then randomly divided into new train, validation, and test sets, following a 70/15/15 split. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA Ge Force RTX 3090 GPU with 24,564 Mi B of memory, utilizing CUDA version 12.3. |
| Software Dependencies | No | The paper mentions 'CUDA version 12.3' but does not list other key software dependencies with specific version numbers, such as Python or deep learning frameworks like PyTorch or TensorFlow. |
| Experiment Setup | Yes | Experimental and hyperparameter details regarding the implementation of the method are in Appendices C and D. Hyperparameters used to produce the results in Table 2 are reported in Tables 6. |