Answers.Body: <p>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.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-17T00:23:44.457Z
Answers.LastActivityDate: 2021-07-17T00:23:44.457Z
Answers.UserDisplayName: meliksahturker
Body: <p>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.</p>
  <p>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).</p>
  <p>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)?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-11T16:39:19.087Z
Comments.Text: I would suggest using statistical methods like ARIMA even before machine learning.
Comments.UserDisplayName: Arun Aniyan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-13T16:46:18.633Z
Comments.Text: I have tried VARMA, since here I have multiple Time Series. But, the RMSE was very high.
Comments.UserDisplayName: Varazda
Tags: <deep-learning><long-short-term-memory><time-series><multilayer-perceptrons><forecasting>
Title: Why doesn't the LSTM model improve the time-series forecasting significantly with respect to the MLP model?
UserDisplayName: Varazda
ThreadText: # Why doesn't the LSTM model improve the time-series forecasting significantly with respect to the MLP model?
  
  (Asked by: username_0 on 2021-07-10)
  
  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)?
  
  **Tags:** deep-learning, long-short-term-memory, time-series, multilayer-perceptrons, forecasting
  > **Comments:**
  > **username_1**: I would suggest using statistical methods like ARIMA even before machine learning.  
  > **username_0**: I have tried VARMA, since here I have multiple Time Series. But, the RMSE was very high.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2021-07-17. Score: 1
  
  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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