Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling
Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling
复制标题
周期性时变回归中的动态因素及其在每小时电力负荷建模中的应用
DOI：
10.1016/j.csda.2011.04.002
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Marius Ooms
中科院分区：
文献类型：
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作者：
V. Dordonnat;S. J. Koopman;Marius Ooms
考虑了一个逐时数据的动态多元周期回归模型。非独立小时单变量时间序列表示为具有24个回归方程的每日多变量时间序列模型。回归系数随方程（或小时）而不同，并随天数随机变化。由于一个不受限制的模型包含许多未知参数，一个有效的方法是在状态空间框架内，施加共同的动态因素的参数，驱动不同的方程的动态。因子模型方法导致更精确的系数估计。一个基本版本的模型的模拟研究表明，对一组单变量基准模型的精度增加。实证研究是一个很长的时间序列的法国国家每小时的电力负荷与天气变量和日历变量作为回归。从信号提取和预测的角度讨论了实证结果。
A dynamic multivariate periodic regression model for hourly data is considered. The dependent hourly univariate time series is represented as a daily multivariate time series model with 24 regression equations. The regression coefficients differ across equations (or hours) and vary stochastically over days. Since an unrestricted model contains many unknown parameters, an effective methodology is developed within the state–space framework that imposes common dynamic factors for the parameters that drive the dynamics across different equations. The factor model approach leads to more precise estimates of the coefficients. A simulation study for a basic version of the model illustrates the increased precision against a set of univariate benchmark models. The empirical study is for a long time series of French national hourly electricity loads with weather variables and calendar variables as regressors. The empirical results are discussed from both a signal extraction and a forecasting standpoint.