With the arrival of the big data era, the interval-valued time series (ITS) has become a research hot spot. This study proposes a novel hybrid model combining the auto-regressive integrated moving average (ARIMA) and regression tree (RT) models for the ITS. Following the idea of hybrid ‘linear and nonlinear’ modeling framework, the ARIMA and RT models capture the linear and nonlinear components hidden in the ITS. The proposed ARIMA-RT model is compared with other competitors through a simulated experiment and a real ITS. Based on the experimental analysis, we find that the performance of the proposed ARIMA-RT model is strikingly superior to other competitors, notably in forecasting the nonlinear ITS. It indicates that the ARIMA-RT model has strong ability to capture the nonlinear ITS in stock markets.

随着大数据时代的到来，区间值时间序列（ITS）已成为研究热点。这项研究提出了一种新颖的混合模型，该模型结合了ITS的自回归综合移动平均（ARIMA）和回归树（RT）模型。遵循“线性和非线性”混合建模框架的思想，ARIMA和RT模型捕获了ITS中隐藏的线性和非线性分量。拟议的ARIMA-RT模型通过模拟实验和真实的ITS与其他竞争对手进行了比较。基于实验分析，我们发现，所提出的ARIMA-RT模型的性能明显优于其他竞争对手，尤其是在预测非线性ITS方面。这表明ARIMA-RT模型具有捕获股票市场中非线性ITS的强大能力。