Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting
Authors: Jingru Fei, Kun Yi, Wei Fan, Qi Zhang, Zhendong Niu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on eight time series forecasting benchmarks consistently demonstrate our model s superiority in both effectiveness and efficiency compared to state-of-the-art methods. 5 Experiments |
| Researcher Affiliation | Academia | 1Beijing Institute of Technology 2State Information Center of China 3University of Oxford 4Tongji University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical formulations, but it does not include any explicitly labeled pseudocode or algorithm blocks. Figure 2 is an architectural diagram, not pseudocode. |
| Open Source Code | Yes | Code https://github.com/aikunyi/Amplifier |
| Open Datasets | Yes | We conduct extensive experiments on eight popular datasets, including ETT datasets (Zhou et al. 2021), Electricity (Wu et al. 2021), Exchange (Lai et al. 2018), Traffic (Sen, Yu, and Dhillon 2019) and Weather (Wu et al. 2021). |
| Dataset Splits | No | The paper mentions lookback window size and prediction lengths, but it does not explicitly provide details about training, validation, and test dataset splits (e.g., percentages or specific methodologies). While Appendix C is mentioned for more dataset details, the provided text does not include it. |
| Hardware Specification | Yes | All experiments in this study were carried out using Py Torch on one single NVIDIA RTX 3070 GPU with 8GB. |
| Software Dependencies | No | All experiments in this study were carried out using Py Torch on one single NVIDIA RTX 3070 GPU with 8GB. The paper mentions PyTorch but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We use Mean Squared Error (MSE) as the loss function and report the results using both MSE and Mean Absolute Error (MAE) as evaluation metrics. We set the lookback window size L as 96 and the prediction length as ω {96, 192, 336, 720}. |