This paper proposes the quantile unit-log-symmetric autoregressive moving average (QULS--ARMA) model for bounded time series on the open unit interval $(0,1)$. The model extends the unit-log-symmetric family by introducing a quantile-based reparameterization and embedding autoregressive and moving-average dynamics directly in the conditional quantile, thereby overcoming limitations of mean-based approaches and providing a coherent framework for proportion data arising from ratios of dependent positive variables. The proposed specification accommodates asymmetric behavior and heavy tails through flexible log-symmetric kernels, including the normal and Student-$t$ distributions. Parameter estimation is carried out via conditional maximum likelihood, and asymptotic properties are established. Monte Carlo simulations and an empirical application to hydroelectric energy storage proportions in Brazil assess the finite-sample performance and practical advantages of the QULS--ARMA model. The results show the good performance of the proposed estimators across a range of scenarios and kernel specifications.

翻译：本文针对定义在开单位区间$(0,1)$上的有界时间序列，提出了分位数单位对数对称自回归移动平均（QULS-ARMA）模型。该模型通过引入基于分位数的再参数化，并将自回归和移动平均动态直接嵌入条件分位数中，拓展了单位对数对称族，从而克服了基于均值方法的局限性，并为由相依正变量比值产生的比例数据提供了一个统一的建模框架。所提出的模型通过灵活的对数对称核函数（包括正态分布和Student-$t$分布）来适应非对称行为和厚尾特征。参数估计采用条件极大似然方法，并建立了其渐近性质。蒙特卡洛模拟以及针对巴西水力发电储能比例的实证应用，评估了QULS-ARMA模型在有限样本下的表现及其实际优势。结果表明，所提出的估计量在多种场景和核函数设定下均表现出良好的性能。