Body: <p>I am trying to implement time series classification, but I am struggling a bit with the fact that my <strong>multivariate data</strong> has <strong>mixed frequencies</strong>. I have about 10 variables that are updated every <strong>minute</strong>, 10 features that are updated every <strong>2-3 hours</strong>, and some low frequency variables that are updated every day.</p>
  <p>The prediction frequency should be quite high at <strong>15 minutes</strong>. So the obvious way might be to group variables within 15 minute intervals. However, this works well for the high frequency variables that occur each minute, but not for the low frequency as within 15 minutes no new values occur.</p>
  <p>Maybe you know something that could help me and give me some sort of starting point?</p>
  <p>I had an idea, but I have never seen such an approach in practice. For the medium and low frequency variables, could we just go back 3h and 1 day respectively, starting from the time of prediction, and aggregate the data in that interval. One could then form several of these packages. However, this would mean that one could recalculate the input sequence for each time point and not simply send a new data point as input into the model which has retained its state from the previous inputs.</p>
  <p>Another idea would be to simply pass in the last raw values of the variables along with some timestamp when they occured. However, I'm a bit afraid that the timestamp introduce some unwanted correlation.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-07-05T07:40:39.273Z
Comments.Text: Is the low frequency data inherently slow changing, or is it faster changing but only sampled at a low rate? Is it actually meaningful to predict new values for it with 15 minutes intervals?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-07-05T07:44:25.370Z
Comments.Text: no i don't think it makes sense to predict new values for it with 15 minutes intervals it is rather slowly changing
Comments.UserDisplayName: Ai4l2s
Tags: <machine-learning><recurrent-neural-networks><long-short-term-memory><time-series>
Title: LSTM Ensemble: Combine low, mid, and high frequency time series data
UserDisplayName: Ai4l2s
ThreadText: # LSTM Ensemble: Combine low, mid, and high frequency time series data
  
  (Asked by: username_0 on 2023-07-05)
  
  I am trying to implement time series classification, but I am struggling a bit with the fact that my **multivariate data** has **mixed frequencies**. I have about 10 variables that are updated every **minute**, 10 features that are updated every **2-3 hours**, and some low frequency variables that are updated every day.
  
  The prediction frequency should be quite high at **15 minutes**. So the obvious way might be to group variables within 15 minute intervals. However, this works well for the high frequency variables that occur each minute, but not for the low frequency as within 15 minutes no new values occur.
  
  Maybe you know something that could help me and give me some sort of starting point?
  
  I had an idea, but I have never seen such an approach in practice. For the medium and low frequency variables, could we just go back 3h and 1 day respectively, starting from the time of prediction, and aggregate the data in that interval. One could then form several of these packages. However, this would mean that one could recalculate the input sequence for each time point and not simply send a new data point as input into the model which has retained its state from the previous inputs.
  
  Another idea would be to simply pass in the last raw values of the variables along with some timestamp when they occured. However, I'm a bit afraid that the timestamp introduce some unwanted correlation.
  
  **Tags:** machine-learning, recurrent-neural-networks, long-short-term-memory, time-series
  
  > **Comments:**
  > **username_1**: Is the low frequency data inherently slow changing, or is it faster changing but only sampled at a low rate? Is it actually meaningful to predict new values for it with 15 minutes intervals?  
  > **username_0**: no i don't think it makes sense to predict new values for it with 15 minutes intervals it is rather slowly changing  
  
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  ## 0 answers
  
  