Postgres has very good storage for time series data - arrays. I remember, I used it 20 years ago for this purpose. You can transform these data to table with unnest function. The arrays use transparent compression.
Timescaledb use it internally with massive partitioning and with some extending optimizer and planner - so using an arrays is transparent for users. TimescaleDB is very good product, smart solution, and extending Postgres's possibilities very well. Postgres has only generic compressions, but Timescale supports special compression's techniques designed for time series data - that is much more effective. It is really good product.
The work with time series data is usually much simpler than work with general data. Time series data (and queries) has clean characteristic and these data are append only. It's not too hard to write time series database. There are lot of products from of this kind. The benefit of timescale is the fact, that it is extension of Postgres. So if you know Postgres, you know timescaledb. And against other time series databases, it is SQL based - so if you know SQL, you don't need to learn new special language.
Attention - lot of SQL databases supports temporal data, but it is different than time series data.
