Investigating Pattern Neurons in Urban Time Series Forecasting
Authors: Chengxin Wang, Yiran Zhao, shaofeng cai, Gary Tan
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that PN-Train considerably improves forecasting accuracy for low-frequency events while maintaining high performance for high-frequency events. Extensive experiments demonstrate that PN-Train significantly improves the forecasting accuracy of state-of-the-art methods across real-world datasets. |
| Researcher Affiliation | Academia | Chengxin Wang Yiran Zhao Shaofeng Cai Gary Tan National University of Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1: Pattern Neuron Guided Training Method |
| Open Source Code | Yes | The code is available at https://github.com/cwang-nus/PN-Train. |
| Open Datasets | Yes | We perform experiments on two real-world datasets from two urban scenarios: Metro Traffic (Hogue, 2019) and Pedestrian (Fang et al., 2024). Detailed dataset statistics are provided in Appendix A.1. |
| Dataset Splits | Yes | We split the dataset chronologically into training, validation, and test sets in a 6:2:2 ratio. |
| Hardware Specification | Yes | All experiments are conducted using Py Torch (Paszke et al., 2019) on a single NVIDIA A100 80GB GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' and 'Adam W optimizer (Loshchilov & Hutter, 2019)' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The look-back window L and forecasting horizon H are both set to 12. The selective ratio ϵ is 0.5, with a pattern neuron detection sample length B of 30 and a fine-tuning sample length R of 10. We split the dataset chronologically into training, validation, and test sets in a 6:2:2 ratio. During training, the UTSM is optimized using the Adam W optimizer (Loshchilov & Hutter, 2019) with a learning rate α1 of 0.001. Early stopping is applied with a patience of 20 epochs, and the maximum number of epochs is set to 300. For pattern neuron optimization, the UTSM is fine-tuned using the same optimizer with a learning rate α2 of 0.002 for one epoch. The batch size was 32. |