model = Sequential()
    model.add(tf.keras.layers.Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 30)))
    model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.RepeatVector(10))
    model.add(tf.keras.layers.LSTM(200, activation='relu', return_sequences=True))
    model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(100, activation='relu')))
    model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(30)))