Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
Authors: Weiwei Ye, Songgaojun Deng, Qiaosha Zou, Ning Gui
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We instantiate FAN on four widely used forecasting models as the backbone and evaluate their prediction performance improvements on eight benchmark datasets. |
| Researcher Affiliation | Collaboration | 1Central South University 2University of Amsterdam 3Zhejiang Lab |
| Pseudocode | Yes | GPU-friendly Py Torch pseudocode is in Appendix A.2. Listing 1: GPU-Friendly Implimentation of FRL |
| Open Source Code | Yes | Our code is publicly available2. http://github.com/wayne155/FAN |
| Open Datasets | Yes | Datasets. We use eight popular datasets in multivariate time series forecasting as benchmarks, including: (1-4) ETT (Electricity Transformer Temperature) 3[47]... (5) Electricity 4... (6) Exchange Rate 5... (7) Traffic 6... (8) Weather 7... 3https://github.com/zhouhaoyi/ETDataset |
| Dataset Splits | Yes | The split ratio for training, validation, and test sets is set to 7:2:1 for all the datasets. |
| Hardware Specification | Yes | All the experiments are implemented by Py Torch [34] and are conducted for five runs with fixed seeds {1, 2, 3, 4, 5} on NVIDIA RTX 4090 GPU (24GB). |
| Software Dependencies | No | All the experiments are implemented by Py Torch [34]. No version number for PyTorch or other specific libraries mentioned. |
| Experiment Setup | Yes | We used a batch size of 32, a learning rate of 0.0003, and trained each run for 100 epochs, with an early stopper set to patience as 5. For the different baselines, we follow the implementation and settings provided in their official code repository. ADAM [18] as the default optimizer across all the experiments. |