Body: <p>I have an <span class="math-container">$x$</span>-<span class="math-container">$y$</span> plane, inside that plane I have 9 paths <span class="math-container">$(p_1, p_2, \dots, p_3)$</span>. Each path is classified into one of the three classes <span class="math-container">$(c_1, c_2, c_3)$</span>. Each path has 100 coordinates points i.e <span class="math-container">$((x_1, y_1),(x_2, y_2), \dots, (x_n, y_n))$</span>. Totally I have 1800 input coordinate points. Now I am interested in training the LSTM model in such a way that if I feed some test path <span class="math-container">$p_{10}$</span>, the model should be supposed to predict which class it belongs to. This is my problem definition. Regarding this, I have some questions</p>
  <ol>
  <li>First of all, is it necessary to use LSTM models to obtain a solution?</li>
  <li>Are there any other simple models to attain a solution to this problem?</li>
  <li>I did some literature surveys for this kind of problem using LSTM, they are having time has one of the parameters along with <span class="math-container">$(x_1, y_1, t_1)$</span>.</li>
  </ol>
  <p>The paper I have read is &quot;A Single-Shot Approach Using an LSTM for Moving Object Path Prediction&quot;.</p>
  <p>I am a beginner to sequence model neural networks. A link or examples to similar works is very much beneficial.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-28T21:02:24.083Z
Comments.Text: Hello. [Welcome to AI SE](https://ai.stackexchange.com/help/on-topic). [Ideally, each post contains 1 main question](https://meta.stackexchange.com/a/39224/287113). What your main question in this case (so that we know what you're looking for)? By the way, the 3rd point is not a question, but a statement.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-29T05:37:20.183Z
Comments.Text: I am looking for answers/ suggestions for my first two questions.
Comments.UserDisplayName: Vijay Kumar S
Tags: <long-short-term-memory>
Title: Using LSTM model to train spatial inputs
UserDisplayName: Vijay Kumar S
ThreadText: # Using LSTM model to train spatial inputs
  
  (Asked by: username_0 on 2021-01-27)
  
  I have an $x$-$y$ plane, inside that plane I have 9 paths $(p\_1, p\_2, \dots, p\_3)$. Each path is classified into one of the three classes $(c\_1, c\_2, c\_3)$. Each path has 100 coordinates points i.e $((x\_1, y\_1),(x\_2, y\_2), \dots, (x\_n, y\_n))$. Totally I have 1800 input coordinate points. Now I am interested in training the LSTM model in such a way that if I feed some test path $p\_{10}$, the model should be supposed to predict which class it belongs to. This is my problem definition. Regarding this, I have some questions
  
  1. First of all, is it necessary to use LSTM models to obtain a solution?
  2. Are there any other simple models to attain a solution to this problem?
  3. I did some literature surveys for this kind of problem using LSTM, they are having time has one of the parameters along with $(x\_1, y\_1, t\_1)$.
  
  The paper I have read is "A Single-Shot Approach Using an LSTM for Moving Object Path Prediction".
  
  I am a beginner to sequence model neural networks. A link or examples to similar works is very much beneficial.
  
  **Tags:** long-short-term-memory
  
  > **Comments:**
  > **username_1**: Hello. [Welcome to AI SE](https://ai.stackexchange.com/help/on-topic). [Ideally, each post contains 1 main question](https://meta.stackexchange.com/a/39224/287113). What your main question in this case (so that we know what you're looking for)? By the way, the 3rd point is not a question, but a statement.  
  > **username_0**: I am looking for answers/ suggestions for my first two questions.  
  
  ---
  
  ## 0 answers
  
  