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2019年4月11日台湾政治大學吳柏林教授讲座预告
( 来源:   发布日期:2019-04-03 阅读:次)

讲座题目:Interval Forecasting on Big Data Context

人:吳柏林 台灣政治大學

讲座时间:2019月4月11日(周四)15:30-17:00

讲座地点:综合楼601会议室

主讲人简介:吳柏林博士,台灣政治大學應數系教授,中華創新資訊與應用統計學會理事長。IJITAS期刊主編。美國Indiana University-Bloomington统计学博士。美國傅布萊特(Fulbright)研究學者獎。英國劍橋大學經濟系客座研究教授、美國史丹佛大學經濟系客座研究教授、韓國國立首爾大學統計系客座教授。日本早稻田大學情報資訊研究所客座教授。

主張應先考慮其數列走勢的特性,如結構改變,圖形識別與認定的問題。結合非線性時間數列和人工智能,應用在實證資料特徵分析與優質預測。提出大模式庫(big models base)建構流程與認定法則。提出區間預測方法,在經濟財金實務上更是一大創新與突破。

内容摘要: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。


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