We introduce a class of semiparametric time series models (SemiParTS) driven by a latent factor process. The proposed SemiParTS class is flexible because, given the latent process, only the conditional mean and variance of the time series are specified. These are the primary features of SemiParTS: (i) no parametric form is assumed for the conditional distribution of the time series given the latent process; (ii) it is suitable for a wide range of data: non-negative, count, bounded, binary, and real-valued time series; (iii) it does not constrain the dispersion parameter to be known. The quasi-likelihood inference is employed in order to estimate the parameters in the mean function. Here, we derive explicit expressions for the marginal moments and for the autocorrelation function of the time series process so that a method of moments can be employed to estimate the dispersion parameter and also the parameters related to the latent process. Simulated results that aim to check the proposed estimation procedure are presented. Forecasting procedures are proposed and evaluated in simulated and real data. Analyses of the number of admissions in a hospital due to asthma and a total insolation time series illustrate the potential for practical situations that involve the proposed models.

我们介绍了一类由潜在因子过程驱动的半参数时间序列模型（SemiPartTS）。提议的 SemiParTS 类是灵活的，因为在给定潜在过程的情况下，只指定了时间序列的条件均值和方差。这些是 SemiPartS 的主要特征：（i）给定潜在过程的时间序列的条件分布不假定参数形式；(ii) 适用于广泛的数据：非负、计数、有界、二进制和实值时间序列；(iii) 它不限制已知的色散参数。采用拟似然推理来估计均值函数中的参数。这里，我们推导出边缘矩和时间序列过程的自相关函数的显式表达式，以便可以采用矩方法来估计离散参数以及与潜在过程相关的参数。模拟结果旨在检查建议的估计程序。预测程序是在模拟和真实数据中提出和评估的。对因哮喘入院的人数和总日照时间序列的分析说明了涉及建议模型的实际情况的潜力。