Quantitative model comparison for Time Series predictive models

时间序列预测模型的定量模型比较

• 批准号: 2207821
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2207821
• 关键词: Quantitative; model; comparison; Time; Series

With more and more machine learning and artificial intelligence technologies being adopted into decision making processes, success control and validation is becoming a matter of increasing concern for end users of modern data science. Of particular concern are reliable answers to questions such as 'does the new model perform better than the state-of-art one?' and 'is the artificial intelligence-based decision making process really better than the standard one', for which replicable, quantitative and generalizable automated testing workflows are sought. Closely related is the question of automated building meta-strategies for data integration, pipelining, and modelling including prediction making or quantification of uncertainty.This project will address the major contemporary challenges around model validation and successcontrol in decision making, in:I. Theory of quantitative model comparison and automated model building, with a focus onheterogeneous, structured or hierarchical modelling tasks such as for panel or spatio-temporal data.II. Software engineering of automated modelling and model validation workflows implementedas modular toolbox environments in R and/or python.III. Deployment in real world applications in the energy, engineering, geography and healthdomains, and development of best practices and validation principles.

随着越来越多的机器学习和人工智能技术被应用到决策过程中，成功控制和验证正成为现代数据科学最终用户越来越关注的问题。特别值得关注的是诸如“新模型的性能是否比最先进的模型更好？”等问题的可靠答案。以及“基于人工智能的决策过程真的比标准决策过程更好吗”，为此，寻求可复制的、定量的和可推广的自动化测试工作流程。与此密切相关的是自动构建数据集成，流水线和建模元策略的问题，包括预测或量化的不确定性。定量模型比较和自动化模型构建的理论，重点是异构，结构化或层次化建模任务，如面板或时空数据。自动建模和模型验证工作流程的软件工程在R和/或Python中实现为模块化工具箱环境。在能源、工程、地理和健康领域的真实的世界应用中进行部署，并制定最佳实践和验证原则。