This survey delves into the application of diffusion models in time-series
forecasting. Diffusion models are demonstrating state-of-the-art results in
various fields of generative AI. The paper includes comprehensive background
information on diffusion models, detailing their conditioning methods and
reviewing their use in time-series forecasting. The analysis covers 11 specific
time-series implementations, the intuition and theory behind them, the
effectiveness on different datasets, and a comparison among each other. Key
contributions of this work are the thorough exploration of diffusion models'
applications in time-series forecasting and a chronologically ordered overview
of these models. Additionally, the paper offers an insightful discussion on the
current state-of-the-art in this domain and outlines potential future research
directions. This serves as a valuable resource for researchers in AI and
time-series analysis, offering a clear view of the latest advancements and
future potential of diffusion models.

本研究调查了扩散模型在时间序列预测中的应用，介绍了扩散模型的背景信息、条件方法以及其在时间序列预测中的应用，并对 11 个具体的时间序列实现进行了分析、比较和介绍。本研究在扩散模型在时间序列预测中的应用和对当前领域的最新研究方向提供了全面的探索，为人工智能和时间序列分析领域的研究人员提供了有价值的资源。

时间序列预测中扩散模型的崛起

The Rise of Diffusion Models in Time-Series Forecasting

The flexibility and effectiveness of message passing based graph neural
networks (GNNs) induced considerable advances in deep learning on
graph-structured data. In such approaches, GNNs recursively update node
representations based on their neighbors and they gain expressivity through the
use of node and edge attribute vectors. E.g., in computational tasks such as
physics and chemistry usage of edge attributes such as relative position or
distance proved to be essential. In this work, we address not what kind of
attributes to use, but how to condition on this information to improve model
performance. We consider three types of conditioning; weak, strong, and pure,
which respectively relate to concatenation-based conditioning, gating, and
transformations that are causally dependent on the attributes. This
categorization provides a unifying viewpoint on different classes of GNNs, from
separable convolutions to various forms of message passing networks. We provide
an empirical study on the effect of conditioning methods in several tasks in
computational chemistry.

本研究针对使用节点和边缘属性提高图神经网络性能的三种条件方法（弱条件、强条件和纯条件）进行了实证研究，并将其应用于计算化学的多项任务进行分析。

图神经网络中条件方法的探索

An Exploration of Conditioning Methods in Graph Neural Networks

This paper proposes a method for improved CTC inference with searched
intermediates and multi-pass conditioning. The paper first formulates
self-conditioned CTC as a probabilistic model with an intermediate prediction
as a latent representation and provides a tractable conditioning framework. We
then propose two new conditioning methods based on the new formulation: (1)
Searched intermediate conditioning that refines intermediate predictions with
beam-search, (2) Multi-pass conditioning that uses predictions of previous
inference for conditioning the next inference. These new approaches enable
better conditioning than the original self-conditioned CTC during inference and
improve the final performance. Experiments with the LibriSpeech dataset show
relative 3%/12% performance improvement at the maximum in test clean/other sets
compared to the original self-conditioned CTC.

本文提出了一种使用搜索的中间结果和多次条件的改进 CTC 推断方法，通过提出新的条件方法，如搜索中间结果和多次通过条件，使得 CTC 推断中的条件能力更强，相对于原本的自我条件的 CTC，在 LibriSpeech 数据集上测试表现有明显提高。