The M# competitions have usually shown very old forecasting algorithms work quite well, with frankly, way less training overhead and data. Ensemble models usually do best, but for a lot of use cases, DL is probably overkill versus ARIMA or triple exponential smoothing.
This is quite untrue. DL methods work well when there’s a lot of data in closed domains. It works well by learning from corpuses of text and media where it can make reasonable interpolations.
When you don’t have enough data and you don’t have a known foundational model that you can do zero shot from, DL doesn’t work better than simpler conventional methods.
There are many optimal, or known near optimal, direct design algorithms for systems that are well characterized. Not only do you get a solution with guaranteed qualities, but any analysis, including verification of reliability and accuracy, and valid operating regimes, of such solutions go way beyond what deep learning models provide.
Second, just assuming that a new algorithm is better than a previous algorithm, without testing, is completely irrational. An anathema to the entire purpose and benefits of science, math, and rational research in general.
If you are going to propose switching algorithms for a long known class of problems, you need to measure the new algorithm against the previous state of the art.
Otherwise people will drive into unnecessary deadends, deploy pointlessly bad designs, forget important knowledge that still has value, and worst of all, lose out on what the older algorithms can suggest for improving newer algorithms, with no excuse but carelessness.
Furthermore, often pre-DL methods can be recast as hand-tuned special cases of DL models - some sequence of linear operations with hand-picked discontinuities sprinkled around. If you can implement the pre-DL method using standard neural network components, then gradient descent training of a neural network "should" find an equivalent or better solution.