FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
Authors: Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrate the effectiveness of our Flash ST across different spatio-temporal prediction tasks using diverse urban datasets. and We conduct extensive experiments on four distinct types of spatio-temporal data tasks to evaluate the effectiveness of our proposed framework. |
| Researcher Affiliation | Academia | Zhonghang Li 1 2 Lianghao Xia 2 Yong Xu 1 3 Chao Huang* 2 1South China University of Technology 2University of Hong Kong 3PAZHOU LAB. |
| Pseudocode | No | The paper describes its methodology using textual descriptions and mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/HKUDS/Flash ST. |
| Open Datasets | Yes | In the pre-training phase, we use four datasets (PEMS03, PEMS04, PEMS07, and PEMS08 (Song et al., 2020)) as our training sets. [...] During the subsequent prompt-tuning phase, we concentrate on four distinct target datasets to fine-tune and evaluate our framework: PEMS07(M) (Yu et al., 2018), CA-D5 (Liu et al., 2023b), Cheng Du-DIDI (Lu et al., 2022), and NYC Citi Bike (Ye et al., 2021). |
| Dataset Splits | Yes | Each target dataset is partitioned into training, validation, and test sets in a ratio of 6:2:2. |
| Hardware Specification | No | The paper discusses training time and computational efficiency, but it does not provide specific details about the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as programming languages, libraries, or frameworks with their version numbers. |
| Experiment Setup | Yes | We configure the model with 32 hidden units for d, dt, and dr, while employing a spatial and temporal encoder with 2 layers. The pre-training phase involves alternating training for 300 epochs on the four pre-training datasets. Subsequently, we conduct fine-tuning for 20 epochs during the prompt-tuning phase. In both the baseline and full fine-tuning setups, the maximum number of training epochs is limited to 100. Furthermore, a batch size of 64 is employed for both phases and all baselines. |