The industry is rich in cases when we are required to make forecasting for
large amounts of time series at once. However, we might be in a situation where
we can not afford to train a separate model for each of them. Such issue in
time series modeling remains without due attention. The remedy for this setting
is the establishment of a foundation model. Such a model is expected to work in
zero-shot and few-shot regimes. However, what should we take as a training
dataset for such kind of model?
Witnessing the benefits from the enrichment of NLP datasets with
artificially-generated data, we might want to adopt their experience for time
series. In contrast to natural language, the process of generation of synthetic
time series data is even more favorable because it provides full control of
series patterns, time horizons, and number of samples. In this work, we
consider the essential question if it is advantageous to train a foundation
model on synthetic data or it is better to utilize only a limited number of
real-life examples. Our experiments are conducted only for regular time series
and speak in favor of leveraging solely the real time series. Moreover, the
choice of the proper source dataset strongly influences the performance during
inference. When provided access even to a limited quantity of short time series
data, employing it within a supervised framework yields more favorable results
than training on a larger volume of synthetic data. The code for our
experiments is publicly available on Github
https://github.com/sb-ai-lab/synthesize_or_not.

该研究通过实验证明，在正常时间序列情况下，仅使用真实时间序列的数量有限的训练数据，相较于使用更多的合成数据进行训练，利用监督框架进行训练可以获得更好的结果。

基于基础的时间序列模型：合成与否？

Towards Foundation Time Series Model: To Synthesize Or Not To  Synthesize?

Consider a policymaker who wants to decide which intervention to perform in
order to change a currently undesirable situation. The policymaker has at her
disposal a team of experts, each with their own understanding of the causal
dependencies between different factors contributing to the outcome. The
policymaker has varying degrees of confidence in the experts' opinions. She
wants to combine their opinions in order to decide on the most effective
intervention. We formally define the notion of an effective intervention, and
then consider how experts' causal judgments can be combined in order to
determine the most effective intervention. We define a notion of two causal
models being \emph{compatible}, and show how compatible causal models can be
merged. We then use it as the basis for combining experts' causal judgments. We
also provide a definition of decomposition for causal models to cater for cases
when models are incompatible. We illustrate our approach on a number of
real-life examples.

考虑一个政策制定者如何将专家的因果判断结合起来，以决定最有效的干预措施。作者定义了有效干预的概念，并研究了如何将专家的因果判断相结合，提出了两个因果模型兼容性的定义，并以实际案例来说明该方法。