Time Series Prediction with Different Sources
"Different Customers - Same Product" data gives an insight to you . But each product data (in each customer) may have its own characteristics.
You have to check if data distribution same in each customer. If not, please get the data of the product for each customers and do the math.
For the Time-Series approach, i would say you can try many algorithms in kats and facebook prophet itself and check the results-Backtesting (MAPE is good for your problem). Look at the best one (minimum error) and implement it.
First of all, I'm not experienced in model training so please be gentle :)
I need to make some time series predictions to handle an issue related to my product. If the value is too low my customer ask for a quick fix and they cannot know that until it happens. I have collected some data related to different customers but the same product.
Data_1




time
value




2020-09-20
600


2020-09-21
450


2020-09-22
350


2020-09-23
300


2020-09-24
150


2020-09-25
50




Data_2




time
value




2020-09-20
50


2020-09-21
600


2020-09-22
550


2020-09-23
400


2020-09-24
200


2020-09-25
50




When the value hits 50, we change the product and it's value goes 600.  I tried the prophet and kats from facebook and they predict on training data. What I want is train with data_1 & data_2 & data_3 ... and predict with data_4 that can start from 50-600 depending on customer. What would be your approach?
TLDR
Same product,different sources. Same dates,different values. Cut them from 50-600 points or combine them all? How to approach ML model?