为大规模城市数据预测时空情境化MLP-Mixers
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
AI总结：
本研究提出NexuSQN，一种基于MLP-Mixer的时空情境化模型，用于大规模城市数据预测，在交通基准上媲美SOTA，并已部署于百度项目预测北京、上海拥堵。
摘要：
时空交通数据（STTD）展现出复杂的相关结构。为有效预测，已设计出大量先进技术来捕捉这些结构。然而，由于STTD通常规模庞大，从业者需要使用计算高效的模型来平衡有效性与效率。基于多层感知器（MLP）的替代范式MLP-Mixer兼具简洁性与有效性。受其在其他领域成功的启发，我们提出了一个改编版本，名为NexuSQN，用于大规模STTD预测。我们首先识别了直接应用MLP-Mixers作为序列和窗口多值性时所面临的挑战。为区分空间与时间模式，随后提出了ST-情境化的概念。我们的结果令人惊讶地表明，这种简单而有效的解决方案在多个交通基准测试中可与最先进（SOTA）基线相媲美。此外，NexuSQN在能源和环境数据等不同领域展示了其多功能性，并已部署在与百度的合作项目中，用于预测北京和上海等特大城市的拥堵情况。我们的发现有助于探索用于实际STTD预测的简单而有效的模型。
英文摘要：
Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive advanced techniques have been designed to capture these structures for effective forecasting. However, because STTD is often massive in scale, practitioners need to strike a balance between effectiveness and efficiency using computationally efficient models. An alternative paradigm based on multilayer perceptron (MLP) called MLP-Mixer has the potential for both simplicity and effectiveness. Taking inspiration from its success in other domains, we propose an adapted version, named NexuSQN, for STTD forecast at scale. We first identify the challenges faced when directly applying MLP-Mixers as seriesand window-wise multivaluedness. To distinguish between spatial and temporal patterns, the concept of ST-contextualization is then proposed. Our results surprisingly show that this simple-yeteffective solution can rival SOTA baselines when tested on several traffic benchmarks. Furthermore, NexuSQN has demonstrated its versatility across different domains, including energy and environment data, and has been deployed in a collaborative project with Baidu to predict congestion in megacities like Beijing and Shanghai. Our findings contribute to the exploration of simple-yet-effective models for real-world STTD forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
