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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)?

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  • $\begingroup$ I would suggest using statistical methods like ARIMA even before machine learning. $\endgroup$
    – Arun Aniyan
    Jul 11, 2021 at 16:39
  • $\begingroup$ I have tried VARMA, since here I have multiple Time Series. But, the RMSE was very high. $\endgroup$
    – Varazda
    Jul 13, 2021 at 16:46

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RNNs are known to be superior to MLP in case of sequential data, like yours. But complex models like LSTM and GRU require a lot of data to achieve their potential. I don't know about your data but you can try to validate your architecture, approach and overall setting using a different, known time-series benchmark data. Maybe something is wrong with architecture, loss function, data, etc... So trying a different but known benchmark data can give you an idea about why you are unable to produce superior results with LSTM.

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