Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Investigating Pattern Neurons in Urban Time Series Forecasting
Authors: Chengxin Wang, Yiran Zhao, shaofeng cai, Gary Tan
ICLR 2025 | Venue PDF | 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. |