Kevin Li, Graham Tierney, Christoph Hellmayr, Mike West
{"title":"Compositional Dynamic Modelling for Counterfactual Prediction in Multivariate Time Series","authors":"Kevin Li, Graham Tierney, Christoph Hellmayr, Mike West","doi":"10.1002/asmb.2908","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Theoretical developments in sequential Bayesian analysis of multivariate dynamic models underlie new methodology for counterfactual prediction. This extends the utility of existing models with computationally efficient methodology, enabling routine exploration of post-intervention analyses with multiple time series in putatively casual studies. Methodological contributions also define the concept of outcome adaptive modelling to monitor and respond to changes in experimental time series following interventions. The benefits of sequential analyses with time-varying parameter models for such investigations are inherited in this broader setting. A case study in forecasting retail revenue following marketing interventions highlights the methodological advances.</p>\n </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Stochastic Models in Business and Industry","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asmb.2908","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Theoretical developments in sequential Bayesian analysis of multivariate dynamic models underlie new methodology for counterfactual prediction. This extends the utility of existing models with computationally efficient methodology, enabling routine exploration of post-intervention analyses with multiple time series in putatively casual studies. Methodological contributions also define the concept of outcome adaptive modelling to monitor and respond to changes in experimental time series following interventions. The benefits of sequential analyses with time-varying parameter models for such investigations are inherited in this broader setting. A case study in forecasting retail revenue following marketing interventions highlights the methodological advances.
期刊介绍：
ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process.
The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
