@alenas
I assume you are discussing time-series data such as sales over time; my understanding is the fast.ai tabular api separates temporal information into date-parts like day of week, time of day, etc. and treats it as categorical and feeds into a linear network. For attentional models it requires an RNN framework. My experience has been treating time-series data from an RNN point-of-view can be very successful but only in situations where there is sufficient data which varies from problem to problem. Definitely worth experimenting with, it has brought me success in the past! See this thread https://forums.fast.ai/t/time-series-sequential-data-study-group/29686/19 for more discussion on DL approaches to time-series classification/forecasting!
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