Quantitative model comparison for Time Series predictive models

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。特别值得关注的是对诸如“新型号是否比最先进的型号性能更好”等问题的可靠回答？以及“基于人工智能的决策过程真的比标准的决策过程好吗？”，对于标准的决策过程，人们寻求可复制的、定量的和可推广的自动化测试工作流程。密切相关的是自动化构建元策略的问题，用于数据集成、流水线和建模，包括不确定性的预测或量化。该项目将解决围绕模型验证和决策成功控制的主要当代挑战，在：1。定量模型比较和自动模型构建的理论，侧重于异质的、结构化的或分层的建模任务，如面板或时空数据。自动化建模和模型验证工作流的软件工程，在R和/或python中作为模块化工具箱环境实现。在能源、工程、地理和健康领域的实际应用中进行部署，并制定最佳实践和验证原则。

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.