Im building a forecast using an LSTM in tensorflow 2.
My data consists of 7 columns: date (daily), gross_sales (the target), daily_total_inventory, avg_daily_order_value, daily_total_new_customers, is_holiday (1/0), is_promo_day (1/0).
My aim is to predict gross sales based on the historical values of past gross sales and all other variables (inventory, avg order value, new customers, whether it was a holiday, and whether there was a promotion going on).
However, I also have a planned schedule for future promotional days that I want my model to take into account when predicting gross sales. For example, every time there is a promotion going on, sales increase significantly compared to non-sale days. So when my model is making predictions, it needs to consider whether or not that future day will have a promotion or not. However I'm not sure how to shape this data, or appropriately load it into an LSTM.