对比基础模型与传统方法，评估其在城市出行预测中的表现
Timeseries Foundation Models for Mobility: A Benchmark Comparison with Traditional and Deep Learning Models
- 用TimeGPT等时序基础模型进行零样本预测，无需重新训练
- 在纽约和维也纳的共享单车数据上，长周期（24小时）预测效果优于传统方法
- 适合对快速部署、少标注场景下的出行预测研究者参考
人群与流量预测在出行数据科学中被广泛研究。传统方法依赖统计模型如ARIMA，后由ST-ResNet等深度学习方法补充。近年来，时间序列基础模型如TimeGPT、Chronos和LagLlama出现。其关键优势在于可直接用于新任务的零样本预测，无需再训练。本研究基于纽约市和奥地利维也纳的两个共享单车数据集，评估TimeGPT相较于传统方法在城市级出行时间序列预测中的表现。模型性能在短（1小时）、中（12小时）和长（24小时）预测时段下进行评估。结果表明，基础模型在出行预测中具有潜力，但也揭示了实验中的局限性。
原文摘要 · Abstract (English)
Crowd and flow predictions have been extensively studied in mobility data science. Traditional forecasting methods have relied on statistical models such as ARIMA, later supplemented by deep learning approaches like ST-ResNet. More recently, foundation models for time series forecasting, such as TimeGPT, Chronos, and LagLlama, have emerged. A key advantage of these models is their ability to generate zero-shot predictions, allowing them to be applied directly to new tasks without retraining. This study evaluates the performance of TimeGPT compared to traditional approaches for predicting city-wide mobility timeseries using two bike-sharing datasets from New York City and Vienna, Austria. Model performance is assessed across short (1-hour), medium (12-hour), and long-term (24-hour) forecasting horizons. The results highlight the potential of foundation models for mobility forecasting while also identifying limitations of our experiments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。