Deep learning based time series modeling and financial forecasting

该项目致力于在时间序列建模方面取得突破，开发新的统计模型和推理技术，重点是金融时间序列数据的建模。这些进步将通过跨学科的研究来实现，结合机器学习、贝叶斯计算、金融计量经济学和日益可用的大数据的最新进展。研究结果将为分析时间序列和理解时间效应提供一系列行之有效的新方法。所开发的方法将使财务预测和风险管理更加准确，并为更广泛的科学界分析时间序列数据开辟新的视野。

This project pursues breakthroughs in time series modelling and develops novel statistical models and inference techniques, with a focus on modelling of financial time series data. The advances will be achieved through interdisciplinary research, combining recent advances in machine learning, Bayesian computation, financial econometrics and the increasing availability of Big Data. The outcomes will provide a new range of proven and powerful approaches for analysing time series and understanding time effects. The methodologies developed will lead to a greater accuracy in financial forecasting and risk management, and open up new horizons for the wider scientific community to analyse time series data.