How to use RNN to predict the next 4 timetsteps using 6 timesteps - deep-learning

Question

I got a dataset with 6 datapoints +4 datapoints as labels, they asked to predict those 4 timesteps using the 6 datasteps.
can you please advise me what model and how should I use it , I though about some kind of RNN since there is time for each point.
Thanks!

Answer 1

These sort of problems where the predictions depend on the previous inputs are generally uses RNN networks(rnn, gru and lstm) as they retain the previous state information.
for deeper understanding:
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Please go through the comments as well I have written in the code.
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.keras import Model
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import RNN, LSTM
"""
creating a toy dataset
lets use this below ```input_sequence``` as the sequence to make data points.
as per the question, we will use 6 points to predict next 4 points
"""
input_sequence = [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10]
X_train = []
y_train = []
#first 6 points will be our input data points and next 4 points will be data label.
# so on we will shift by 1 and make such data points and label pairs
for i in range(len(input_sequence)-9):
X_train.append(input_sequence[i:i+6])
y_train.append(input_sequence[i+6:i+10])
X_train = np.array(X_train, dtype=np.float32)
y_train = np.array(y_train, dtype=np.int32)))
#X_test for the predictions (contains 6 points)
X_test = np.array([[8,9,10,1,2,3]],dtype=np.float32)
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
#we will be using basic LSTM, which accepts input in ```[num_inputs, time_steps, data_points], therefore reshaping as per that```
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
x_points = X_train.shape[-1]
print("one input contains {} points".format(x_points))
model = Sequential()
model.add(LSTM(4, input_shape=(1, x_points)))
model.add(Dense(4))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
model.fit(X_train, y_train, epochs=500, batch_size=5, verbose=2)
output = list(map(np.ceil, model.predict(X_test)))
print(output)
we have used the simpler model, this further can be improved to get better results.