Research Initiation Award: Predicting and Characterizing Noisy Time Series

研究启动奖：预测和表征噪声时间序列

• 批准号: 9309786
• 负责人: Andreas Weigend
• 金额: $ 10万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-01 至 1997-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9309786&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Predicting; Characterizing

9309786 Weigend The progress in the last decade in predicting and understanding time series has been remarkable. Where once time series analysis was shaped by linear systems theory, it is now possible to recognize when an apparently complicated time series has been produced by a low- dimensional nonlinear system, characterize its essential properties, and build a model that can be used for prediction. At the opposite extreme, there is now a much richer framework for designing algorithms such as neural networks that can learn and adapt to the structure in time series that do not have a simple origin. This research addresses the following three questions: How to estimate the accuracy in time series prediction, how to deal with data sets that are noisy and chaotic, and how to characterize the system that temporal sequence by analyzing the predictive model. The tools to be developed in response to these questions combine recent advances from connectionism and dynamical systems theory. They will be evaluated on real-world data, submitted by various groups for consideration at the Time Series Prediction and Analysis Competition that was held under the auspices of the Santa Fe Institute. ***

9309786 Weigend在过去十年中，在预测和理解时间序列方面取得了显着的进展。 一旦时间序列分析是由线性系统理论形成的，现在就可以识别出低维非线性系统何时产生了明显复杂的时间序列，描述其基本属性，并建立可用于预测的模型。 在另一个极端，现在有一个更丰富的框架来设计算法，如神经网络，可以学习和适应没有简单起源的时间序列结构。 本研究解决了以下三个问题：如何估计时间序列预测的准确性，如何处理噪声和混沌的数据集， 以及如何通过分析预测模型来刻画时间序列的系统。 为回答这些问题而开发的工具结合了联合收割机和动力系统理论的最新进展。 将根据各团体提交的供在圣菲研究所主持下举行的时间序列预测和分析竞赛上审议的真实世界数据对它们进行评价。 ***