我们被要求翻译一篇名为《StyleTime: Style Transfer for Synthetic Time Series Generation》的论文的Introduction部分。用户提供了论文的arXiv链接，并上传了内容，我们需要从中提取Introduction部分并进行中文翻译。

首先，从用户提供的文本中，我们可以看到论文的完整内容，但我们需要定位到“1 INTRODUCTION”部分。根据文本，Introduction部分从“1 INTRODUCTION”开始，结束于“2 BACKGROUND”之前。

仔细阅读用户提供的内容，Introduction部分的原文如下：

"1
INTRODUCTION
Synthetic generation of labeled images has successfully been ap-
plied to improve generalization capability, and, therefore, perfor-
mance of machine learning image detection algorithms. Little work,
however, has been done on using synthetic time series to improve
generalization capability of machine learning forecasting algo-
rithms. In financial services, for example, there is interest in aug-
menting historical stock price time series with synthetic rare event
data in order to enable price prediction models generalize better on
unseen scenarios during the unexpected market shocks due to eco-
nomic events, global pandemic, flash crashes, etc. . One generally
expects models trained on such augmented time series to perform
well in practice if synthetic time series is statistically similar to
real out-of-sample data as suggested by the “train on synthetic,
test on real" (TSTR) framework [8]. Moreover, statistical realism
of the augmented time series dataset is often a requirement for
explainability purposes in applications such as finance.

Multi-agent simulation provides a means of generating synthetic
time series using a bottom-up approach. In multi-agent market
simulation, the resultant time series originate from interaction of a
large number of market agents (e.g., [4]). Because of the flexibility
to change market configuration on the agent level, multi-agent
simulation is well-suited for modeling counterfactual scenarios,
however, it is notoriously difficult to calibrate to reflect realistic
properties of the market [22].

In this paper, we propose to use neural style transfer (NST) to
improve statistical realism of synthetic time series, and use it to
stylize time series that model counterfactual scenarios for the pur-
pose of data augmentation. NST was initially introduced in [12] to
incorporate artistic style into photographs and quickly won over
the imaging community. By including the feature correlations of
multiple layers, one can obtain a stationary, multi-scale represen-
tation of the input image (i.e., the Gram matrix), which captures
its texture information but not the global arrangement – which is
called style representation. Despite receiving attention by the com-
puter vision community, there is little academic work on NST in
the context time series. In [20], NST was used to introduce realistic
noise to the seismic shock model. In [19], denoising autoencoder
combined with NST was used as a generative technique for realistic
daily price time series. The generated time series showed statistical
properties similar to historical data; additionally, the visual inspec-
tion of generated paths indicated presence of technical patterns
that are characteristic of the historical daily asset time series. Both
of the aforementioned methods extract time series style represen-
tations in the same manner they are extracted for images in [12],
which may not be suitable notions of styles for realistic time series
since they only contain information about correlations across ex-
tracted features. Further related work also includes NST for audio
and voice synthesis. This class of NST methods utilize a variety of
different techniques for extracting style representations including
frequency-domain representations (e.g., VGG-19 or wide-shallow-
random networks applied to 2D spectrograms [21]), time-domain
representations (e.g., SoundNet encoder [3] or WaveNet decoder
[17] applied to raw waveforms), and domain expert knowledge of
auditory perception [13]. Unfortunately, since these style represen-
tations are specific to audio data, they cannot be directly applied to
other domains, such as finance."

现在，我们需要将这段英文翻译成中文。翻译时要注意：

1. 保持学术论文的严谨性和专业性。
2. 术语要准确，如“neural style transfer”译为“神经风格迁移”，“synthetic time series”译为“合成时间序列”。
3. 长句要合理拆分，保持中文表达习惯。
4. 注意参考文献标记（如[8]、[12]等）保留原格式。

翻译如下：