How to implement Many to Many LSTM architecture for numerical data (not timeseries , not NLP) in Keras

Question

I have read this, this

I have numerical data in arrays of shape,

input_array = 14674 x 4

output_array = 13734 x 4

reshaping for LSTM (batch, timesteps, features) gives

input_array= (14574, 100, 4)

output_array = (13634, 100, 4)

Now I would like to build a Many to Many LSTM architecture for this given data, should I use encoder-decorder or synced sequence input and output architecture

using following model but it works on when input and outputs are same

import tenowingsorflow
from tensorflow.keras.metrics import Recall, Precision
from tensorflow.keras.layers import Conv1D, Dense, MaxPooling1D, Flatten

opt = tensorflow.keras.optimizers.Adam(learning_rate=0.001)


model_enc_dec_cnn = Sequential()
model_enc_dec_cnn.add(Conv1D(filters=64, kernel_size=9, activation='relu', input_shape=(100, 4)))
model_enc_dec_cnn.add(Conv1D(filters=64, kernel_size=11, activation='relu'))
model_enc_dec_cnn.add(MaxPooling1D(pool_size=2))
model_enc_dec_cnn.add(Flatten())
model_enc_dec_cnn.add(RepeatVector(100))
model_enc_dec_cnn.add(LSTM(100, activation='relu', return_sequences=True))
model_enc_dec_cnn.add(TimeDistributed(Dense(4)))

model_enc_dec_cnn.compile( optimizer=opt, loss='mse',             metrics=['accuracy']) 
history = model_enc_dec_cnn.fit(X,y, epochs=3, batch_size=64,    )