Approximation Theory of Convolutional Architectures for Time Series Modelling
Authors: Haotian Jiang, Zhong Li, Qianxiao Li
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the approximation properties of convolutional architectures applied to time series modelling, which can be formulated mathematically as a functional approximation problem. In the recurrent setting, recent results reveal an intricate connection between approximation efficiency and memory structures in the data generation process. In this paper, we derive parallel results for convolutional architectures, with Wave Net being a prime example. Our results reveal that in this new setting, approximation efficiency is not only characterised by memory, but also additional fine structures in the target relationship. |
| Researcher Affiliation | Academia | 1Department of Mathematics, National University of Singapore 2School of Mathematical Science, Peking University 3Institute of High Performance Computing, A*STAR, Singapore. |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any statements about releasing code or links to source code repositories for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on approximation properties of architectures for time series modeling, formulated as a functional approximation problem. It does not use or refer to any specific publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with datasets, thus no train/validation/test splits are mentioned. |
| Hardware Specification | No | This is a theoretical paper focusing on approximation theory; it does not mention any specific hardware used for conducting experiments. |
| Software Dependencies | No | This is a theoretical paper; it does not mention any specific software dependencies with version numbers used for implementation or experimentation. |
| Experiment Setup | No | This is a theoretical paper; it does not describe an experimental setup, including hyperparameters or system-level training settings. |