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 [1].
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
| 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. |