How can Keras predict sequences of sales (individually) of 11106 distinct customers, each a series of varying length (anyway from 1 to 15 periods)

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I am approaching a problem that Keras must offer an excellent solution for, but I am having problems developing an approach (because I am such a neophyte concerning anything for deep learning). I have sales data. It contains 11106 distinct customers, each with its time series of purchases, of varying length (anyway from 1 to 15 periods).

I want to develop a single model to predict each customer's purchase amount for the next period. I like the idea of an LSTM, but clearly, I cannot make one for each customer; even if I tried, there would not be enough data for an LSTM in any case---the longest individual time series only has 15 periods.

I have used types of Markov chains, clustering, and regression in the past to model this kind of data. I am asking the question here, though, about what type of model in Keras is suited to this type of prediction. A complication is that all customers can be clustered by their overall patterns. Some belong together based on similarity; others do not; e.g., some customers spend with patterns like $100-$100-$100, others like $100-$100-$1000-$10000, and so on.

Can anyone point me to a type of sequential model supported by Keras that might handle this well? Thank you.

I am trying to achieve this in R. Haven't been able to build a model that gives me more than about .3 accuracy.

lstm keras deep-learning r
2 Answers

Hi here's my suggestion and I will edit it later to provide you with more information

Since its a sequence problem you should use RNN based models: LSTM, GRU's

user_102770420

I don't think the main difficulty is coming from which model to use as much as how to frame the problem.

As you mention, "WHO" is spending the money seems as relevant as their past transaction in knowing how much they will likely spend. But you cannot train 10k+ models either for each customers.

Instead I would suggest clustering your customers base, and instead trying to fit a model by cluster, using all the time series combined for the customers in that cluster to train the same model.

This would allow each model to learn the spending pattern of that particular group. For that you can use LTSM or RNN model.

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