  Effective utilization of time series data is often constrained by the
scarcity of data quantity that reflects complex dynamics, especially under the
condition of distributional shifts. Existing datasets may not encompass the
full range of statistical properties required for robust and comprehensive
analysis. And privacy concerns can further limit their accessibility in domains
such as finance and healthcare. This paper presents an approach that utilizes
large language models and data source interfaces to explore and collect time
series datasets. While obtained from external sources, the collected data share
critical statistical properties with primary time series datasets, making it
possible to model and adapt to various scenarios. This method enlarges the data
quantity when the original data is limited or lacks essential properties. It
suggests that collected datasets can effectively supplement existing datasets,
especially involving changes in data distribution. We demonstrate the
effectiveness of the collected datasets through practical examples and show how
time series forecasting foundation models fine-tuned on these datasets achieve
comparable performance to those models without fine-tuning.
