In my aviation safety work, deep learning outperforms traditional non-DL models for multivariate time-series forecasting. Between deep learning models, I've had a wide variance in performance between transformers, Bi-LSTMs, regular MLPs, VAEs, and so on.
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.
