Answers.Body: <p>I don't know what kind of price data you're dealing with. I suppose the order of the data matters a lot, so my suggestion would be:</p>
  
  <ol>
  <li><p>Use LSTM as it handles time series better</p></li>
  <li><p>You can predict 3 consecutive numbers from an RNN as the next three days' predictions</p></li>
  <li><p>Try regression first, it is likely it will not work (or just flatten the curves, depend on your data noise), then classification is an easier approach</p></li>
  <li><p>Don't forget normalization </p></li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-10T00:32:57.380Z
Answers.LastActivityDate: 2018-08-10T00:32:57.380Z
Answers.UserDisplayName: Kevin. Fang
Body: <p>When working with time-series data, is it wrong to use daily prices as features and the price after 3 days as the target?</p>
  <p>Or should I use the next-day price as a target, and, after training, predict 3 times, each time for one more day ahead (using the predicted value as a new feature)?</p>
  <p>Will these 2 approaches give similar results?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-08-23T14:57:04.600Z
Comments.Text: Have you considered trying out both apporaches on your training data and comparing which one performs better?
Comments.UserDisplayName: don_pablito
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-01T09:25:51.257Z
Comments.Text: This question was also asked [here](https://datascience.stackexchange.com/q/36518).
Comments.UserDisplayName: nbro
Tags: <long-short-term-memory><datasets><time-series><features><labels>
Title: When working with time-series data, is it wrong to use different time-steps for the features and target?
UserDisplayName: George
ThreadText: # When working with time-series data, is it wrong to use different time-steps for the features and target?
  
  (Asked by: username_0 on 2018-08-08)
  
  When working with time-series data, is it wrong to use daily prices as features and the price after 3 days as the target?
  
  Or should I use the next-day price as a target, and, after training, predict 3 times, each time for one more day ahead (using the predicted value as a new feature)?
  
  Will these 2 approaches give similar results?
  
  **Tags:** long-short-term-memory, datasets, time-series, features, labels
  
  > **Comments:**
  > **username_1**: Have you considered trying out both apporaches on your training data and comparing which one performs better?  
  > **username_2**: This question was also asked [here](https://datascience.stackexchange.com/q/36518).  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2018-08-10. Score: 1
  
  I don't know what kind of price data you're dealing with. I suppose the order of the data matters a lot, so my suggestion would be:
  
  1. Use LSTM as it handles time series better
  2. You can predict 3 consecutive numbers from an RNN as the next three days' predictions
  3. Try regression first, it is likely it will not work (or just flatten the curves, depend on your data noise), then classification is an easier approach
  4. Don't forget normalization
  
  
  ---
  
  