讲座题目：Interval Forecasting on Big Data Context

主 讲 人：台湾政治大学吴柏林教授

讲座时间：2019年4月12日（周五）13：30-14：30

讲座地点：6号学院楼402会议室

主办单位：数据科学学院

摘 要：

Purpose: The object of this research is to construct an optimal internal forecasting method on big data context. Design/methodology/approach: An intelligent model construction, including consumer behavior and market information, structural changes detection, nonlinear pattern recognition, spatial causality, semantic processing mode is presented. Findings: The major drawback in forecasting field is that the statistical forecasting result is derived from historical data but it often encounters non-realistic problem when people predict future trends or market changes in real world. Practical Applications: Construction of Big Data platform will be a new technique provides to solve the structured change and uncertain problems. According to the artificial intelligence evolution and on line improvement to the market conditions, it will do a better performance to prevailing future event. Originality: We efficiently integrate the idea of structure change, entropy and market behavior in the forecasting process. Conclusion: Since historical time series analysis has difficult to prove the relationship/causality with future events. Especially in the case of a structural change, the future is full of high uncertainty, ambiguity and unexpected.

主讲人简介：

吴柏林博士，台湾政治大学应用数学系教授，中华创新信息与应用统计学会理事长。IJITAS期刊主编。美国Indiana University-Bloomington统计学博士。美国傅布莱特(Fulbright)研究学者奖。英国剑桥大学经济系客座研究教授、美国史丹佛大学经济系客座研究教授、韩国国立首尔大学统计系客座教授。日本早稻田大学情报信息研究所客座教授。

吴柏林教授主张应先考虑其数列走势的特性，如结构改变，图形识别与认定的问题。结合非线性时间数列和人工智能，应用在实证资料特征分析与优质预测。提出大模式库(big models base)建构流程与认定法则。提出区间预测方法，在经济财金实务上更是一大创新与突破。

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