Time series data are crucial across diverse domains such as finance and healthcare, where accurate forecasting and decision-making rely on advanced modeling techniques. While generative models have shown great promise in capturing the intricate dynamics inherent in time series, evaluating their performance remains a major challenge. Traditional evaluation metrics fall short due to the temporal dependencies and potential high dimensionality of the features. In this paper, we propose the REcurrent NeurAL (RENAL) Goodness-of-Fit test, a novel and statistically rigorous framework for evaluating generative time series models. By leveraging recurrent neural networks, we transform the time series into conditionally independent data pairs, enabling the application of a chi-square-based goodness-of-fit test to the temporal dependencies within the data. This approach offers a robust, theoretically grounded solution for assessing the quality of generative models, particularly in settings with limited time sequences. We demonstrate the efficacy of our method across both synthetic and real-world datasets, outperforming existing methods in terms of reliability and accuracy. Our method fills a critical gap in the evaluation of time series generative models, offering a tool that is both practical and adaptable to high-stakes applications.

翻译：时间序列数据在金融和医疗等众多领域中至关重要，其精确预测和决策依赖于先进的建模技术。尽管生成模型在捕捉时间序列固有的复杂动态特性方面展现出巨大潜力，但其性能评估仍面临重大挑战。传统评估指标因时间依赖性和特征潜在的高维性而存在不足。本文提出循环神经网络拟合优度检验，这是一种用于评估时间序列生成模型的新型统计严谨框架。通过利用循环神经网络，我们将时间序列转换为条件独立的数据对，从而能够对数据中的时间依赖性应用基于卡方的拟合优度检验。该方法为评估生成模型质量提供了稳健且理论依据充分的解决方案，特别适用于时间序列有限的情境。我们在合成数据集和真实数据集上验证了该方法的有效性，其在可靠性和准确性方面均优于现有方法。本方法填补了时间序列生成模型评估的关键空白，提供了一种既实用又能适应高风险应用场景的工具。