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.

DMS9626113 Chen这项研究与非线性时间序列分析中的非参数模型构建程序和非参数预测方法有关。 这项研究的第一个目的是为非线性时间序列开发新的非参数建模程序。 研究人员研究了功能系数自回归模型，并使该模型在实践中更易于使用。 特别是，研究了加权的局部线性回归程序。 该过程与曲线拟合的经典局部线性回归有所不同，其中响应函数感兴趣。 在这里，估计系数功能是主要感兴趣的。 还研究了一种检测系数函数中不连续性的程序。 这项研究的第二个目标与使用非参数平滑技术进行多步预测有关。 研究者研究了多阶段非参数预测指标的性质，该特性与多步预测的迭代整合程序密切相关。 初步研究表明，新方法确实提高了预测的准确性。 第一个目标是证明预测变量适用于广泛的非线性AR模型。 第二个目标是研究该方法的实际实施，尤其是自动带宽选择方法和预测策略。 这项研究与非线性时间序列分析中的模型构建程序和预测方法有关。 时间序列是在一段时间内观察到的一组数据。 例如，用于环境研究的每日臭氧和污染物读数，经济研究的季度失业率或GNP和嘈杂的电信信号都是时间序列分析的主题。 时间序列分析试图揭示观察到的时间序列的生成机制，并提供明智的方法来根据当前和过去的信息来预测未来的观察。 线性TI ME系列模型假定未来的观察结果与简单线性函数中的当前和过去观察有关，而非线性模型则假设了复杂的关系。 在这项研究中，研究者遵循“让数据自言自语”的原则，并为非线性时间序列开发建模程序。 它用于克服选择适当模型的实际应用中遇到的困难。 这项研究的第二个目标与非线性时间序列的多步预测有关。非线性时间序列模型已被证明在多步预测中具有与线性模型的一定优势。 在这项研究中，研究者研究了提高预测准确性的新预测因子的特性。 有充分的理由相信，这项研究的结果应该在非线性时间序列分析中有重大贡献，该分析在经济学，电信，气象学，环境等领域中具有许多重要的应用。