Body: <p>I have an input vector <span class="math-container">$X$</span>, which contains a series of measurements within a period, e.g. 100 measurements in 1 sec. The goal is to predict an event, let's say, moving forward, backward or static. </p>
  
  <p>I don't want to predict the output just by looking at one series of measurements, but by looking at a window of <span class="math-container">$n$</span> vectors <span class="math-container">$X$</span> of measurements, making it dependant on the previous measurements, because of the noise in the measurements.</p>
  
  <p>Is there a way RNN can help me with this? Many to one architecture? LSTM? 
  CNN of 1D + LSTM + dense? </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-20T02:13:51.693Z
Comments.Text: Hi and welcome to AI SE. So, to clarify, you want to feed to the model _at once_ $n$ vectors $X_1, X_2, \dots, X_n$, each of which contains e.g. $M=100$ measurements, i.e. $|X_i| = 100$, for all $i$?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-20T02:46:01.163Z
Comments.Text: Hi, basically I want to predict the class based on these X vector with 100 measuments, but also depend on the previous X inputs I fed. Is there a model which takes into account previous states? Or do I need to feed 'n' number of X in a certain window of states? I heard about RNN and Lstm.
Comments.UserDisplayName: Erick Medina
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-20T22:38:06.587Z
Comments.Text: Yes, RNN may be helpful for your problem. Please, edit your post to answer my questions above. Also, don't just ask "Is there a way RNN can help me with this?", because people could say either "yes" or "no". You could ask "How specifically could I design an LSTM to solve this problem?" What should the input of this LSTM be? What should be the output? And so on. These are more specific questions.
Comments.UserDisplayName: nbro
Tags: <convolutional-neural-networks><recurrent-neural-networks><long-short-term-memory><time-series>
Title: How to predict an event (or action) based on a window of time-series measurements?
UserDisplayName: Erick Medina
ThreadText: # How to predict an event (or action) based on a window of time-series measurements?
  
  (Asked by: username_0 on 2020-03-20)
  
  I have an input vector $X$, which contains a series of measurements within a period, e.g. 100 measurements in 1 sec. The goal is to predict an event, let's say, moving forward, backward or static.
  
  I don't want to predict the output just by looking at one series of measurements, but by looking at a window of $n$ vectors $X$ of measurements, making it dependant on the previous measurements, because of the noise in the measurements.
  
  Is there a way RNN can help me with this? Many to one architecture? LSTM?
  CNN of 1D + LSTM + dense?
  
  **Tags:** convolutional-neural-networks, recurrent-neural-networks, long-short-term-memory, time-series
  
  > **Comments:**
  > **username_1**: Hi and welcome to AI SE. So, to clarify, you want to feed to the model _at once_ $n$ vectors $X_1, X_2, \dots, X_n$, each of which contains e.g. $M=100$ measurements, i.e. $|X_i| = 100$, for all $i$?  
  > **username_0**: Hi, basically I want to predict the class based on these X vector with 100 measuments, but also depend on the previous X inputs I fed. Is there a model which takes into account previous states? Or do I need to feed 'n' number of X in a certain window of states? I heard about RNN and Lstm.  
  > **username_1**: Yes, RNN may be helpful for your problem. Please, edit your post to answer my questions above. Also, don't just ask "Is there a way RNN can help me with this?", because people could say either "yes" or "no". You could ask "How specifically could I design an LSTM to solve this problem?" What should the input of this LSTM be? What should be the output? And so on. These are more specific questions.  
  
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  ## 0 answers
  
  