Towards Foundation Time Series Model: To Synthesize Or Not To
  Synthesize?
Kseniia Kuvshinova Olga Tsymboi Alina Kostromina Dmitry Simakov Elizaveta Kovtun
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
\url{https://github.com/sb-ai-lab/synthesize_or_not}.