通过分层循环切换状态模型发现协调时间序列中的群体动态
Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models
AI总结：
提出分层循环切换状态模型，无监督学习系统级与个体级动态，线性复杂度，在篮球、士兵等数据上预测性能媲美大型神经网络且更具可解释性。
摘要：
我们寻求一种计算高效的模型，用于处理由多个相互作用的实体（即“智能体”）产生的时间序列集合。现有的跨个体时间模式模型未能明确纳入能够影响个体实体轨迹的系统级集体行为。为弥补文献中的这一空白，我们提出了一种新的分层切换状态模型，该模型可以以无监督方式训练，同时学习系统级和个体级动态。我们采用一个潜在的系统级离散状态马尔可夫链，对潜在的实体级链提供自上而下的影响，而实体级链则控制每个观测时间序列的发射。从观测到实体级和系统级潜在链的循环反馈允许近期情境信息以自下而上的方式影响所有层级的动态展开。我们假设，在群体动态中同时包含自上而下和自下而上的影响将提高所学动态的可解释性，并减少预测时的误差。我们的分层切换循环动态模型可以通过对所有潜在链的闭式变分坐标上升更新来学习，其计算复杂度随实体数量线性增长。这在渐近意义上不比为每个实体单独拟合模型更昂贵。对合成数据和真实篮球团队运动的分析表明，我们精简的参数模型能够与需要更多计算资源的大型神经网络模型相比取得有竞争力的预测结果。对士兵数据以及一个包含64个协作实体的合成任务的进一步实验表明，我们的方法能够随时间产生关于团队动态的可解释见解。
英文摘要：
We seek a computationally efficient model for a collection of time series arising from multiple interacting entities (a.k.a. "agents"). Recent models of temporal patterns across individuals fail to incorporate explicit system-level collective behavior that can influence the trajectories of individual entities. To address this gap in the literature, we present a new hierarchical switching-state model that can be trained in an unsupervised fashion to simultaneously learn both system-level and individual-level dynamics. We employ a latent system-level discrete state Markov chain that provides top-down influence on latent entity-level chains which in turn govern the emission of each observed time series. Recurrent feedback from the observations to the latent chains at both entity and system levels allows recent situational context to inform how dynamics unfold at all levels in bottom-up fashion. We hypothesize that including both top-down and bottom-up influences on group dynamics will improve interpretability of the learned dynamics and reduce error when forecasting. Our hierarchical switching recurrent dynamical model can be learned via closed-form variational coordinate ascent updates to all latent chains that scale linearly in the number of entities. This is asymptotically no more costly than fitting a separate model for each entity. Analysis of both synthetic data and real basketball team movements suggests our lean parametric model can achieve competitive forecasts compared to larger neural network models that require far more computational resources. Further experiments on soldier data as well as a synthetic task with 64 cooperating entities show how our approach can yield interpretable insights about team dynamics over time.
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。
