Hello developers, does this PostgresML handles the time series data?

In a word, yes. If your dataset scale is:

• Sub millions: everything will be sub second, including training and especially inference (can be sub millisecond), even without using best practices like indexes.
• Millions: Vanilla Postgres will be able to train models in seconds at this scale with $100 laptop hardware. You probably don't need to do anything special other than have indexes for inference. As you approach billions you may want to look into query parrallelism. You'll also want to get familiar with EXPLAIN to analyze why queries take longer than you expect.
• Billions: consider partitioning your tables to enable more parallelism, or using the Timescale extension that optimizes for timeseries explicitly to keep blazing fast performance.
• Trillions: you may want to generate summary or rollup tables for individual statistics that will reduce the overall training time
• Quadrillions: there are probably going to be special considerations at this scale, but generally they can be solved by sharding across multiple physical machines. Consider using the Citus extension to make this easier to manage.

There are many ways to perform multivariate time series prediction with PostgresML. The following algorithms are supported:

https://postgresml.org/user_guides/training/algorithm_selection/

For example, here is a blog post detailing how you might use XGBoost to formulate the problem, although this usage of XGBoost is from Python, the steps can be adapted to PostgresML:

https://cprosenjit.medium.com/multivariate-time-series-forecasting-using-xgboost-1728762a9eeb

There is also support for fine tuning deep learning models that have been published to HuggingFace. For example:

https://huggingface.co/spaces/keras-io/timeseries-classification-from-scratch

It might help give more specific answers if you described your objective/dataset/domain in depth.