Please provide some suggestions for multi timeseries training

[eromoe]

Hello,

I am facing some challenges in predicting time-series classification for stock data.
Some are resolvable, though I'm not sure is there any better solution.

1. Many diffierent series : around 4,000 stocks
   • There is a need to support some type of group training, such as company type or industry. ( use Dataset api)
   • Further training needs to be performed according to time segmentation. ( use get_walk_forward_splits , but catergory value varying by time, some value disappear , and new value come. )
2. Unequal Time Lengths:
   • Almost no two stocks share the same time lengths; the starting dates for each stock's data are different. ( if split by time, each range only contain different stocks )
3. Handling Missing Values:
   • Some data points are missing due to a suspension in trading (no trading took place on these days for these particular stocks, although other stocks may have been active).
   • There are also genuine instances of data missing, like some fields in the financial reports. It is not feasible to simply fill in with zeros or the mean value. A dynamic missing value filling method that adjusts over time might be necessary, which I currently don't have a good solution for.

After thinking of these problem, I'm confused about how to get started..