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英国巴斯大学Fotios Petropoulos副教授特邀报告

发布时间: 2019/04/25 16:01:46     点击次数:次   打印本页

【北航经商大讲堂 第28期】

英国巴斯大学Fotios Petropoulos副教授特邀报告

题目: Bagging with Real Data

主讲人:Fotios Petropouplos副教授

英国巴斯大学管理学院International Journal of Forecasting副主编

主持人:康雁飞 副教授

时间: 2019年4月26日16:00-17:00

地点: 北航新主楼A座618

摘要:

The existing portfolio of models within a forecasting software is, very often, not able to capture the true data generating process (DGP). This had led many researchers to combine the forecasts from two or more forecasting models and, on average, achieve better accuracy. One approach to forecasting combination is bootstrapping and aggregation, or bagging. In this approach, the remainder of the data from a decomposition method is bootstrapped and then used to create new instances of the original series. Each new series is forecasted separately and the forecasts are then combined. In this talk, we present a new approach where instead of bootstrapping the remainder to create a new series, we use a large database to find similar time series. These similar series are either forecasted using familiar models, explicitly assuming a DGP, or their “future” values are directly taken as forecasts in which case no DGP is assumed. Our approach is tested on real data and shows promising improvements over standard benchmarks, especially when historical information is limited.

现有的时间序列预测模型通常很难捕捉真实的数据生成过程,这使得很多研究者使用两种或多种预测方法的组合来达到更好的预测精度。一种预测组合的方法是bagging。这种方法通过对时间序列分解之后的残差进行bootstrapping,然后生成原数据的新实例,最后对新生成的数据进行预测组合。在这个报告中,我们展示一种新的方法,这种方法不对残差进行bootstrapping,而是使用一个海量数据库,来寻找相似的时间序列。通过预测这些相似的时间序列(假定数据生成过程)或者直接使用它们的未来数值来做预测(不用假定数据生成过程)。在真实数据上的测试表明这种方法提高了预测精度,尤其是在有限历史信息的情况下,这种方法具有明显优势。

主讲人简介:

Fotios Petropoulos is Associate Professor at the University of Bath, UK, and Associate Editor for theInternational Journal of Forecasting. He is interested in research on time series forecasting, judgmental approaches for forecasting, statistical and judgmental model selection and integrated business forecasting processes. Fotios's research so far has focused on the improvement of forecasting processes and more specifically around two streams. First, he has examined how additional information can be extracted from time series data through time transformation (temporal aggregation) and the use of hierarchies. Second, he has investigated interactions between forecasting and management judgment.

Fotios Petropoulos,英国巴斯大学管理学院副教授,International Journal of Forecasting副主编。他的研究领域包括时间序列预测、预测的判断方法、统计模型选择及商业预测过程。Fotios主要围绕两个方面研究如何提高预测精度:(1)他研究如何通过时间转换和分层来从时间序列数据中提取额外的有价值的信息;(2)他研究预测和管理决策之间的相互关联。