Hey,
I’m trying to forecast the product demand.
(Long-Term-Demand-Forecasting…)
My raw data looks (really simplified) like this:
Date Customer_id article_id indicator1 indicator2 order_size
0 2020-03-01 D021 104 True 213.6 10
1 2020-03-02 D034 243 True 325.2 15
2 2020-03-02 D034 311 False 65.3 43
3 2020-03-02 D054 104 False 853.2 5
4 2020-03-03 D021 554 False 125.8 67
5 2020-03-03 D093 219 True 34.2 11
I want to predict the order_size (maybe separately for every article).
I already written something that combines Embedding for the categorical data with an LSTM.
I really would like to use a RNN, but I don’t have a proper time series since i have multiple rows for one date, because there are multiple orders every day from different customers for different products.
I could just “force” it into one row by replacing the customer_id-column with a "no. of customer that placed an order that day’-column, but then I would lose important information about which customer orders which product, etc.
Any idea how I could manage this without losing information?