<p>I assume that that this is for gaining some confidence about the predictions.</p>

<p>If this is the case, there are multiple ways to do this. For example, refer to <a href="https://arxiv.org/pdf/1711.11053.pdf" rel="nofollow noreferrer">this</a> paper by Amazon on how to predict quantiles, and <a href="https://arxiv.org/pdf/1709.01907.pdf" rel="nofollow noreferrer">this</a> paper on how to use a Bayesian framework to obtain uncertainty around the predictions. </p>

<p>If you have other intentions, please clarify.</p>
Predict set of location points at a single time slot
<p>The sample dataset contains Location point of the user.</p>

<pre><code>df.head()

   user           tslot         Location_point
0   0   2015-12-04 13:00:00     4356
1   0   2015-12-04 13:15:00     4356
2   0   2015-12-04 13:30:00     3659
3   0   2015-12-04 13:45:00     4356
4   0   2015-12-04 14:00:00     8563

df.shape 

(576,3)
</code></pre>

<p>The location points are random and need to predict the next location point of the user for a given time. As the location points are random numbers I need to predict the set of location points at each time slot.</p>

<pre><code>Example:

If I need to predict the location point at tslot 2015-12-04 14:00:00.
my predicted output should be [8563,4356,3659,5861,3486].
</code></pre>

<p>My code</p>

<pre><code>time_steps=1
data_dim = X_train.shape[2]
model = Sequential()
model.add(LSTM(data_dim, input_shape=(time_steps,data_dim), activation='relu'))
model.add(Dense(data_dim))
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.fit(X_train, y_train, epochs=20, batch_size=96)
model.summary()
</code></pre>

<p>which helps to to predict 1 location points for each time slot. I would like to know if this is possible and how?</p>