I have recently started learning time series forecasting. I have a dataset of the weekly payment history of 10k clients over 1 year, and I want to predict the future 5 payments for a test set of 1k clients.
From what I have tried, I've found that using LSTMs instead of a simple MLP doesn't improve the prediction as much as I anticipated. My understanding is that LSTMs captures the relations between time steps, whereas simple MLPs treat each time step as a separated feature (doesn't take succession into consideration).
So, my question is: why doesn't the LSTM model improve the forecasting significantly? What are the best models for such a task, given that the time series are short (maximum sequence's length = 52)?