OK, I almost understand your data frame.
I want to ensure that for each data [x1,x2, …, x460]，the front of 20 features [x1, …, x20] is associated with a timestamp(like Date0)，and [x21,…, x40] is associated with Date1… MeanWhile, I suppose that Date0 < Date1 < … < Date22，which means Date0 is earlier than Date1.
If so, you can use RNN to construct model to clasify craps(10 classes).
step1: split your sample feature to time step
[x1, x2, …, x460]  —> [[x1,x2,…,x20], [x21,x22,…x40],…]
So it can be seen 23 sequences and each item with 20 features.
step2: construct GRU or LSTM model and feed the data
Now, you can feed batch data with size [batch_size, seq_len, feature_dim], which seq_len is 23 and feature_dim is 20 as above.
step3: auto_grad CrossEntropy loss
Just train and eval.
There many RNN model examples in github, follow it and train your model.
Good luck.