I found that for a lot of time-series problems, people often treated them as if they were standard tabular/supervised learning problems. There's a separate page of the report which goes into these in detail: https://mlcontests.com/tabular-data?ref=mlc_reddit

For example, for the Kaggle Amex default prediction competition, the data is time-series in the sense that you're given a sequence of customer statements, and then have to predict the probability of them defaulting within a set time period after that. The winner's solution mostly seemed to flatten the features and use LightGBM, but they did use a GRU for part of their final ensemble: https://www.kaggle.com/competitions/amex-default-prediction/discussion/348111

The M6 forecasting competition finished recently, I'm looking forward to seeing what their winners did: https://m6competition.com/