MG-TSD：具有引导学习过程的多粒度时间序列扩散模型
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
AI总结：
针对扩散模型在概率时间序列预测中的不稳定性问题，提出多粒度时间序列扩散模型MG-TSD，利用数据内在粒度层级作为中间目标引导学习，实现最先进预测性能且无需外部数据。
摘要：
近年来，扩散概率模型因其生成高保真样本的显著能力，在生成式时间序列预测中引起了广泛关注。然而，如何有效利用其强大的建模能力于概率时间序列预测任务中仍是一个开放性问题，部分原因在于其随机性所带来的不稳定性挑战。为解决这一挑战，我们提出了一种新颖的多粒度时间序列扩散（MG-TSD）模型，该模型通过利用数据内在的粒度层级作为中间扩散步骤的给定目标，来引导扩散模型的学习过程，从而实现了最先进的预测性能。构建这些目标的方式源于以下观察：扩散模型的前向过程将数据分布逐步破坏为标准正态分布，这直观上与将细粒度数据平滑为粗粒度表示的过程相一致，两者都会导致细粒度分布特征的逐渐丢失。在本研究中，我们推导了一种新颖的多粒度引导扩散损失函数，并提出了一种简洁的实现方法，以有效利用不同粒度层级上的粗粒度数据。更重要的是，我们的方法不依赖额外的外部数据，使其具有通用性并适用于各个领域。在真实世界数据集上进行的大量实验表明，我们的MG-TSD模型优于现有的时间序列预测方法。
英文摘要：
Recently, diffusion probabilistic models have attracted attention in generative time series forecasting due to their remarkable capacity to generate high-fidelity samples. However, the effective utilization of their strong modeling ability in the probabilistic time series forecasting task remains an open question, partially due to the challenge of instability arising from their stochastic nature. To address this challenge, we introduce a novel Multi-Granularity Time Series Diffusion (MG-TSD) model, which achieves state-of-the-art predictive performance by leveraging the inherent granularity levels within the data as given targets at intermediate diffusion steps to guide the learning process of diffusion models. The way to construct the targets is motivated by the observation that the forward process of the diffusion model, which sequentially corrupts the data distribution to a standard normal distribution, intuitively aligns with the process of smoothing fine-grained data into a coarse-grained representation, both of which result in a gradual loss of fine distribution features. In the study, we derive a novel multi-granularity guidance diffusion loss function and propose a concise implementation method to effectively utilize coarse-grained data across various granularity levels. More importantly, our approach does not rely on additional external data, making it versatile and applicable across various domains. Extensive experiments conducted on real-world datasets demonstrate that our MG-TSD model outperforms existing time series prediction methods.
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