Synthetic time series are often used in practical applications to augment the
historical time series dataset for better performance of machine learning
algorithms, amplify the occurrence of rare events, and also create
counterfactual scenarios described by the time series.
Distributional-similarity (which we refer to as realism) as well as the
satisfaction of certain numerical constraints are common requirements in
counterfactual time series scenario generation requests. For instance, the US
Federal Reserve publishes synthetic market stress scenarios given by the
constrained time series for financial institutions to assess their performance
in hypothetical recessions. Existing approaches for generating constrained time
series usually penalize training loss to enforce constraints, and reject
non-conforming samples. However, these approaches would require re-training if
we change constraints, and rejection sampling can be computationally expensive,
or impractical for complex constraints. In this paper, we propose a novel set
of methods to tackle the constrained time series generation problem and provide
efficient sampling while ensuring the realism of generated time series. In
particular, we frame the problem using a constrained optimization framework and
then we propose a set of generative methods including ``GuidedDiffTime'', a
guided diffusion model to generate realistic time series. Empirically, we
evaluate our work on several datasets for financial and energy data, where
incorporating constraints is critical. We show that our approaches outperform
existing work both qualitatively and quantitatively. Most importantly, we show
that our ``GuidedDiffTime'' model is the only solution where re-training is not
necessary for new constraints, resulting in a significant carbon footprint
reduction.

本文利用一个带限制的优化框架，提出了一套生成方法，包括 “GuidedDiffTime”，用于生成合成的具有现实性的时间序列，相比现有方法更加高效，且无需重新训练，取得了显著的性能提升，并大幅降低了碳足迹。

关于有限时间序列生成问题的研究

On the Constrained Time-Series Generation Problem

We propose a new class of probabilistic neural-symbolic models, that have
symbolic functional programs as a latent, stochastic variable. Instantiated in
the context of visual question answering, our probabilistic formulation offers
two key conceptual advantages over prior neural-symbolic models for VQA.
Firstly, the programs generated by our model are more understandable while
requiring lesser number of teaching examples. Secondly, we show that one can
pose counterfactual scenarios to the model, to probe its beliefs on the
programs that could lead to a specified answer given an image. Our results on
the CLEVR and SHAPES datasets verify our hypotheses, showing that the model
gets better program (and answer) prediction accuracy even in the low data
regime, and allows one to probe the coherence and consistency of reasoning
performed.

本研究提出了一种新的概率神经符号模型，其拥有作为潜在随机变量的符号功能程序，并在视觉问题回答的背景下实例化，相比之前的神经符号模型，我们的模型提供了两个关键概念上的优势，第一，我们的模型生成的程序更易理解，需要较少的教学示例； 第二，我们展示了我们可以向该模型提出反事实情景，来探究其对于给定图像可以导致特定答案的程序的信念，我们对 CLEVR 和 SHAPES 数据集的结果验证了我们的假设，在低数据情况下，模型具有更好的方案（和答案）预测准确性，并允许人们探究所执行推理的连贯性和一致性。