Answers.Body: <p>One approach would be to use a Sequence Processing Neural architecture - one option is a <a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow noreferrer">recurrent network</a>. These were specifically designed with your intent in mind.</p>
  
  <p>They can consume sequences of data and learn from these in order to predict subsequent time-steps. Though these will require you to understand <a href="https://towardsdatascience.com/10-things-to-think-about-before-starting-to-code-your-deep-neural-network-65094a1e7c08" rel="nofollow noreferrer">best practices</a> for implementation.</p>
  
  <p>If your task isn't too complex you could make use of a <a href="https://en.wikipedia.org/wiki/Forward_algorithm" rel="nofollow noreferrer">forward algorithm</a> or some variant. Many of these models make use of simplifying assumptions - if your use case cannot admit these you'll have to go for something more advanced.</p>
  
  <p>If your prediction task is complex and requires <strong>heavy</strong> use of long term dependencies you may have to go for a more cutting edge architecture like a <a href="https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html" rel="nofollow noreferrer">transformer</a> - but I heard that these are difficult to implement and most definitely require familiarity with deep learning systems. </p>
  
  <p>Finally, if you need a system that can understand data and dependencies across <strong>very large</strong> spans of time then you may have to wait for the science to progress.</p>
  
  <p>I hope this helps.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-01T17:05:25.307Z
Answers.LastActivityDate: 2019-09-01T17:05:25.307Z
Answers.UserDisplayName: respectful
Body: <p>I'm new to artificial intelligence. I am looking for the most appropriate AI solution for my application, which is developing an algorithm to predict a proceeding situation (edited: I want my algorithm to predict a situation or more than one to happen at a predefined moment) and, at the same time, to learn from the iterative stages of my application.</p>
  
  <p>Any suggestions? Any help? Any proposals?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-01T22:05:23.960Z
Comments.Text: Hi. What do you mean by "predict a proceeding situation"? What is _proceeding situation_? Edit your post to clarify this.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-03T11:04:13.730Z
Comments.Text: @nbro: done editing.
Comments.UserDisplayName: user28345
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-30T06:31:52.897Z
Comments.Text: I  still o not see any information in your question at all about the type of events to be predicted.
Comments.UserDisplayName: George White
Tags: <ai-design><prediction>
Title: Which predictive algorithm is most appropriate for a proceeding situation?
UserDisplayName: user28345
ThreadText: # Which predictive algorithm is most appropriate for a proceeding situation?
  
  (Asked by: username_0 on 2019-09-01)
  
  I'm new to artificial intelligence. I am looking for the most appropriate AI solution for my application, which is developing an algorithm to predict a proceeding situation (edited: I want my algorithm to predict a situation or more than one to happen at a predefined moment) and, at the same time, to learn from the iterative stages of my application.
  
  Any suggestions? Any help? Any proposals?
  
  **Tags:** ai-design, prediction
  
  > **Comments:**
  > **username_1**: Hi. What do you mean by "predict a proceeding situation"? What is _proceeding situation_? Edit your post to clarify this.  
  > **username_0**: @username_1: done editing.  
  > **username_2**: I  still o not see any information in your question at all about the type of events to be predicted.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2019-09-01. Score: 1
  
  One approach would be to use a Sequence Processing Neural architecture - one option is a [recurrent network](https://en.wikipedia.org/wiki/Recurrent_neural_network). These were specifically designed with your intent in mind.
  
  They can consume sequences of data and learn from these in order to predict subsequent time-steps. Though these will require you to understand [best practices](https://towardsdatascience.com/10-things-to-think-about-before-starting-to-code-your-deep-neural-network-65094a1e7c08) for implementation.
  
  If your task isn't too complex you could make use of a [forward algorithm](https://en.wikipedia.org/wiki/Forward_algorithm) or some variant. Many of these models make use of simplifying assumptions - if your use case cannot admit these you'll have to go for something more advanced.
  
  If your prediction task is complex and requires **heavy** use of long term dependencies you may have to go for a more cutting edge architecture like a [transformer](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html) - but I heard that these are difficult to implement and most definitely require familiarity with deep learning systems.
  
  Finally, if you need a system that can understand data and dependencies across **very large** spans of time then you may have to wait for the science to progress.
  
  I hope this helps.
  
  
  ---
  
  