I have a weekly time series data set as shown here :

date product vendor stock price

10-02-2023 product1 ZZZ No 1344
10-09-2023 product1 ZZZ No 9265
10-16-2023 product1 ZZZ No 7854
.
.
.
05-06-2024 product1 ZZZ No 784

10-02-2023 product2 XXX Yes 564
10-09-2023 product2 XXX … 366
10-16-2023 product2 XXX … 646
.
.
.
05-06-2024 product2 XXX Yes 4556

10-02-2023 product3 ZZZ …
10-09-2023 product3 ZZZ …
10-16-2023 product3 ZZZ …
.
.
.
05-06-2024 product3 ZZZ Yes
.
.

So for each product I have a complete time series data that goes from 10-02-2023 to 05-06-2024 ( i have 1000 products)

I want to built a prediction model that , given a product ‘producti’, predicts the price for the date after 05-06-2024 .

The problem is that in my time series data a date is repeated 1000 times because I have 1000 products; however time series prediction models take as an input a data set where each date figures only onetime .
How shall I do to be able to train my model once at a time ? And which prediction model is suitable to my usecase ?

thank you