I've recently been made aware of this excellent and imo much needed library by @lmmentel.

The reason is its similarity to the datatypes module of sktime, which introduces semantic typing for time series related data types - we distinguish "mtypes" (machine representations) and "scitypes" (scientific types, what visions calls semantic type). More details here as reference.

Few questions for visions devs:

• time series are known to be a notoriously splintered field in terms of data representation, and even more when it comes to learning tasks (as in your ML example). Do you see visions moving in the direction of typing for ML?
• would you have time to look into the sktime datatypes module and assess how similar this is to visions? If similar, we might be tempted to take a dependency on visions and contribute. Key features are mtype conversions, scitype inference, checks that also return metadata (e.g., number of time stamps in a series, which can be represented 4 different ways)