I'm currently working on LSTM autoencoder for anomaly detection. My main problem is I have multiple time series - each individual time series corresponds to a different customer, detailing their sales data from 2020 to 2023. I don't want to aggregate data (I want detect anomaly for each customer) neither make model per customer (thousands of customers means thousands of models)
Example of data:
(obviously there are much more customers than in this simple example)
Please guide me on how to incorporate customer-specific information into the LSTM autoencoder model. How I can model my LSTM autoencoder to add layer about type of customer? Or how to model the data so the model will know that each sequence is from different customer? Or maybe for this kind of data is much more suitable model than LSTM autoencoder?
Thank you in advance for your help!