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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.

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