发表机构
Union County Magnet High School; Kean University（联合县磁石高中; 基恩大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对股票收益预测，提出在线神经进化架构搜索ONE-NAS，在主机进化、边缘端预测，集成模型在树莓派上高效运行，显著超越基线。
AI 中文摘要
准确的预测模型通常规模较大，在线更新成本高昂，且一旦训练完成架构便固定不变。我们将ONE-NAS（一种在线神经进化架构搜索方法，它在每批数据到达时进化一组小型循环网络）应用于日频横截面股票收益预测，并在主机与端点流水线上进行试点：主机运行搜索过程，并通过TCP/IP将每一代冠军基因组发送至树莓派4B，后者进行在线预测。在树莓派上，单个冠军模型预测50只股票的窗口耗时24.6毫秒，而由40个岛屿冠军组成的集成模型耗时556毫秒，远在日度决策周期之内。在2022年至2024年间美国中盘股的四组面板数据上，将种群作为岛屿冠军的秩均值集成进行解读，扣除实际交易成本后净收益为+27.5%，相比之下，在线LSTM、在线GRU和月度重训练的LSTM基线收益为+11.3%至+14.8%，而先前ONE-NAS工作中使用的单一最佳基因组收益为+4.5%。
英文摘要
Accurate forecasting models are usually large, expensive to update online, and fixed in architecture once trained. We apply ONE-NAS, an online neuroevolutionary architecture search that evolves a population of small recurrent networks as each window of data arrives, to daily cross-sectional stock return prediction, and pilot it on a host and endpoint pipeline: the host runs the search and ships each generation's champion genomes over TCP/IP to a Raspberry Pi 4B, which predicts online. On the Pi a single champion predicts a 50-stock window in 24.6~ms and the ensemble of 40 island champions in 556~ms, far inside the daily decision cycle. On four panels of US mid-cap equities over 2022--2024, reading the population as a rank-mean ensemble of island champions returns $+27.5\%$ net of realised transaction costs, against $+11.3$ to $+14.8\%$ for online LSTM, online GRU and monthly-retrained LSTM baselines and $+4.5\%$ for the single best genome used in prior ONE-NAS work.
