I discuss recent research advances in Bayesian state-space modeling of multivariate time series. A main focus is on the “decouple/recouple” concept that enables application of state-space models to increasingly large-scale data, applying to continuous or discrete time series outcomes. Applied motivations come from areas such as financial and commercial forecasting and dynamic network studies. Explicit forecasting and decision goals are often paramount and should factor into model assessment and comparison, a perspective that is highlighted. The Akaike Memorial Lecture is a context to reflect on the contributions of Hirotugu Akaike and to promote new areas of research. In this spirit, this paper aims to promote new research on foundations of statistics and decision analysis, as well as on further modeling, algorithmic and computational innovation in dynamic models for increasingly complex and challenging problems in multivariate time series analysis and forecasting.

中文翻译：

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多元时间序列的贝叶斯预测：可扩展性、结构不确定性和决策

我讨论了多元时间序列贝叶斯状态空间建模的最新研究进展。主要关注的是“解耦/重新耦合”概念，该概念使状态空间模型能够应用于越来越大规模的数据，应用于连续或离散的时间序列结果。应用动机来自金融和商业预测以及动态网络研究等领域。明确的预测和决策目标通常是最重要的，应该考虑到模型评估和比较中，这是一个突出的观点。赤池纪念讲座是反思赤池博土的贡献并促进新研究领域的背景。本着这种精神，本文旨在促进对统计和决策分析基础以及进一步建模的新研究，