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..
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Authors: Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Ning An, Defu Lian, Longbing Cao, Zhendong Niu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate our consistent superiority over state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Beijing Institute of Technology, 2Tongji University, 3University of Oxford 4University of Technology Sydney, 5University of Macau, 6USTC 7He Fei University of Technology, 8Macquarie University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at this repository: https://github.com/aikunyi/Fre TS. |
| Open Datasets | Yes | Our empirical results are performed on various domains of datasets, including traffic, energy, web, traffic, electrocardiogram, and healthcare, etc. Specifically, for the task of short-term forecasting, we adopt Solar 2, Wiki [37], Traffic [37], Electricity 3, ECG [16], METR-LA [38], and COVID-19 [4] datasets, following previous forecasting literature [16]. |
| Dataset Splits | Yes | We split the datasets into training, validation, and test sets by the ratio of 7:2:1 except for the COVID-19 datasets with 6:2:2. |
| Hardware Specification | Yes | Our model is implemented with Pytorch 1.8 [41], and all experiments are conducted on a single NVIDIA RTX 3080 10GB GPU. |
| Software Dependencies | Yes | Our model is implemented with Pytorch 1.8 [41], and all experiments are conducted on a single NVIDIA RTX 3080 10GB GPU. |
| Experiment Setup | Yes | By default, both the frequency channel and temporal learners contain one layer of Fre MLP with the embedding size d of 128, and the hidden size dh is set to 256. For short-term forecasting, the batch size is set to 32 for Solar, METR-LA, ECG, COVID-19, and Electricity datasets. And for Wiki and Traffic datasets, the batch size is set to 4. For the long-term forecasting, except for the lookback window size, we follow most of the experimental settings of LTSF-Linear [35]. The lookback window size is set to 96 which is recommended by FEDformer [30] and Autoformer [14]. We take MSE (Mean Squared Error) as the loss function and report MAE (Mean Absolute Errors) and RMSE (Root Mean Squared Errors) results as the evaluation metrics. |