If you have short time-series with low variance, noise and outliers, strong prior knowledge, or limited resources to train and maintain a model, I would stick with simpler traditional models.

If DL is a good fit for your use-case, then I tend to like transformers or combining CNNs with recurrent models (e.g., BiGRU, GRU, BiLSTM, LSTM) and optional attention.