Nonparametric Modeling and Prediction for Time Series Analysis

时间序列分析的非参数建模和预测

• 批准号: 9626113
• 负责人: Rong Chen
• 金额: $ 6.5万
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-15 至 1999-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9626113&HistoricalAwards=false
• 关键词: Nonparametric; Modeling; Prediction; Time; Series

DMS9626113 Chen This research is concerned with nonparametric model building procedures and nonparametric prediction methods in nonlinear time series analysis. The first objective of this research is to develop a new nonparametric modeling procedure for nonlinear time series. The investigator studies the functional coefficient autoregressive models and makes the model easier to use in practice. In particular, a weighted local linear regression procedure is studied. This procedure differs from the classical local linear regression for curve fitting where the response function is of interest. Here, estimating the coefficient functions are of main interest. A procedure for detecting discontinuities in the coefficient functions is studied as well. The second objective of this research is concerned with multi-step predictions using nonparametric smoothing techniques. The investigator studies the properties of a multi-stage nonparametric predictor, which is closely related to the iterative integration procedures for multi-step prediction. Preliminary study shows that the new method does improve the accuracy of the prediction. The first goal is to show that the predictor is applicable to a wide class of nonlinear AR models. The second goal is to investigate the practical implementation of the method, particularly the automatic bandwidth selection method and prediction strategy. This research is concerned with model building procedures and prediction methods in nonlinear time series analysis. A time series is a set of data observed over a period of time. For example, daily ozone and pollutant readings for environmental study, quarterly unemployment rate or GNP for economical study and noisy telecommunication signals are all subjects of time series analysis. Time series analysis tries to reveal the generating mechanism of the observed time series and to provide sensible methods to predict future observations based on current and past information. Linear ti me series models assumes the future observations relate to the current and past observations in simple linear functions while nonlinear models assume complex relationship. In this research, the investigator follows the principle of `letting the data speak for themselves' and develops modeling procedures for nonlinear time series. It is used to overcome the difficulty encountered in real applications of choosing an appropriate model. The second objective of this research is concerned with multi-step predictions for nonlinear time series. Nonlinear time series models have been shown to have certain advantages in multi-step forecasting over linear models. In this research, the investigator studies the properties of a new predictor that improves the prediction accuracy. There are sufficient reasons to believe that the results of this research should have significant contributions in nonlinear time series analysis, which has many important applications in the fields of economics, telecommunication, meteorology, environment and many others.

本研究主要研究非参数时间序列分析中的非参数建模过程和非参数预测方法。 本研究的第一个目的是发展一个新的非参数非线性时间序列建模程序。 研究了函数系数自回归模型，使模型更易于实际应用。 特别是，加权局部线性回归过程进行了研究。 该过程不同于经典的局部线性回归曲线拟合，其中响应函数是感兴趣的。 在这里，估计系数函数是主要的兴趣。 还研究了检测系数函数不连续性的方法。 本研究的第二个目标是 使用非参数平滑技术的多步预测。 研究了与多步预测的迭代积分过程密切相关的多步非参数预测的性质。 初步研究表明，新方法确实提高了预测精度。 第一个目标是表明，预测是适用于广泛的一类非线性AR模型。 第二个目标是研究该方法的实际实现，特别是自动带宽选择方法和预测策略。 本研究系关于非线性时间序列分析之建模程序与预测方法。 时间序列是在一段时间内观察到的一组数据。 举例来说，环境研究所需的每日臭氧和污染物读数、经济研究所需的每季失业率或本地居民生产总值，以及嘈杂的电讯讯号，都是时间数列分析的对象。 时间序列分析试图揭示 观测时间序列的生成机制，并提供合理的方法来预测未来的观测的基础上，当前和过去的信息。 线性时间序列模型假设未来观测值与当前和过去观测值之间存在简单的线性关系，而非线性模型假设未来观测值与当前和过去观测值之间存在复杂的关系。 在这项研究中，研究者遵循“让数据为自己说话”的原则，开发了非线性时间序列的建模程序。 它是用来克服在真实的应用中遇到的困难，选择一个合适的模型。 本研究的第二个目标是关于非线性时间序列的多步预测。非线性时间序列模型在多步预测中比线性模型具有一定的优势。 在这项研究中，研究人员研究了一种新的预测器，提高预测精度的属性。 有足够的理由相信，本研究的结果应该有显着的贡献，在非线性时间序列分析，这在经济，电信，气象，环境等领域有许多重要的应用。