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 | Conference PDF | Archive PDF | Plain Text | 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. |