inputs = Input((window_size, input_size,))

  H = Conv1D(10, 10, activation=tanh, padding='same', kernel_initializer='glorot_normal')(inputs)
  H = Conv1D(10, 10, activation=tanh, padding='same', kernel_initializer='glorot_normal')(H)
  H = Conv1D(10, 10, activation=tanh, padding='same', kernel_initializer='glorot_normal')(H)
  H = Conv1D(10, 10, activation=tanh, padding='same', kernel_initializer='glorot_normal')(H)
  H = Conv1D(1, 3, activation=tanh, padding='same', kernel_initializer='glorot_normal')(H)
  H = Dropout(0.5)(H)
  H = Flatten()(H)

  H = Dense(100, activation=tanh, kernel_initializer='glorot_normal')(H)

  output = Dense(1 , activation=tanh, kernel_initializer='glorot_normal')(H)

  model = Model(inputs=[inputs], outputs=[output])
  model.compile(loss=rmse, optimizer=Adam(lr=0.001), metrics=[rmse])
  model.summary()