Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
Authors: Qiang Wu, Gechang Yao, Zhixi Feng, Yang Shuyuan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed Peri-mid Former demonstrates outstanding performance in five mainstream time series analysis tasks, including shortand long-term forecasting, imputation, classification, and anomaly detection. The code is available at https://github.com/Wu Qiang XDU/Peri-mid Former. |
| Researcher Affiliation | Academia | Qiang Wu Gechang Yao Zhixi Feng Shuyuan Yang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, China {wu_qiang, yao_gechang}@stu.xidian.edu.cn, {zxfeng, syyang}@xidian.edu.cn |
| Pseudocode | No | The paper describes its methodology using textual descriptions, equations, and flowcharts (e.g., Figure 2, Figure 8) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Wu Qiang XDU/Peri-mid Former. |
| Open Datasets | Yes | A detailed description of the dataset is given in Table 6. Table 6: Dataset descriptions. The dataset size is organized in (Train, Validation, Test). Tasks Dataset Dim Series Length Dataset Size Information (Frequency) ETTm1, ETTm2 7 {96, 192, 336, 720} (34465, 11521, 11521) Electricity (15 mins) |
| Dataset Splits | Yes | A detailed description of the dataset is given in Table 6. Table 6: Dataset descriptions. The dataset size is organized in (Train, Validation, Test). |
| Hardware Specification | Yes | All the deep learning networks are implemented in Py Torch and trained on NVIDIA 4090 24GB GPU. |
| Software Dependencies | No | All the deep learning networks are implemented in Py Torch and trained on NVIDIA 4090 24GB GPU. |
| Experiment Setup | Yes | The detailed experiment configuration is shown in Table 7. Table 7: Experiment configuration of Peri-mid Former. All the experiments use the ADAM [49] optimizer with the default hyperparameter configuration for (β1, β2) as (0.9, 0.999). Tasks / Configurations Model Hyper-parameter Training Process k Layers dmodel LR Loss Batch Size Epochs |