标题: | 一个基于深度学习的动态资料驱动应用系统-以天气预测为例 A Dynamic Data-Driven Application System based on Deep Learning - A Case Study of Weather Forecasting |
作者: | 江启钧 罗济群 林斯寅 Chiang, Chi-Chun Lo, Chi-Chun Lin, Szu-Yin 资讯管理研究所 |
关键字: | 深度学习;关联分析;序列分析;动态资料驱动应用系统;Deep Learning;Association Analysis;Sequence Analysis;Dynamic Data Driven Application Systems |
公开日期: | 2016 |
摘要: | 随着巨量资料趋势崛起,数量庞大、种类繁多、且具即时性的动态资料日益增加,在资料快速变动的环境中,要达到准确且有效率的资讯预测与评估,成为一个相当困难的挑战。深度学习是一个新兴的特征类机器学习方法,以多层次类神经网路的方式进行处理,透过抽象层的特征表示来化简维度。但是使用深度学习方法时,常会有输入资料维度过于庞大、无法在动态环境中执行等问题。在本研究中,将深入探讨如何应用动态资料驱动的概念,从大量且不同组合所产生的时间序列资料中,找到各种资料与预测目标之间的关联性,利用关联分析(Association Analysis)、序列分析(Sequence Analysis)、深度学习(Deep Learning)等方法来设计一个基于深度学习的动态资料驱动应用系统。本研究将以气象资料为例,相较于先前的研究,此系统的平均预测错误率改进了87%。 With the advent of the big data era, dynamic real-time data have increased in both volume and varieties. It is a difficult task to acquire an accurate prediction with respect to rapidly changing data. The Deep Learning (DL) is one of the major approaches of machine learning for feature extraction. It attempts to model high-level abstractions and dimension reduction in data by using multiple processing layers. However, some of the common issues may occur during the implementation process of deep learning, such as: input data having over-complicated dimension, and unable to execute in a dynamic environment. Therefore, it will be helpful if we combine dynamic data-driven concept with DL methods to obtain the dynamic data correlation or relationship between prediction results and actual data in a dynamic environment. This thesis applies the concept of dynamic data-driven to obtain the correlation or relationship between the prediction goals and numbers of different combination results. The methods of association analysis, sequence analysis, and DL are applied to design a dynamic data-driven system based on deep learning. Weather data are used in the experiments. Compare to the previous studies, the proposed system improved the average prediction error rate by 87%. |
URI: | http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070353431 http://hdl.handle.net/11536/143346 |
显示于类别: | Thesis |