How to train an unlabled data in machine learning?

I have the data of almost 9000 entities, I want to train my model and detect anomalies from data.

I tried few of things to did my work, and one thing I done is

def create_sequences(values, time_steps=TIME_STEPS):
 output = []
 for i in range(len(values) - time_steps):
     output.append(values[i : (i + time_steps)])
 return np.stack(output)

here I start splitting my training data

x_train = create_sequences(data['HR'].values)
x_train = np.expand_dims(x_train,axis=2)

x_train = create_sequences(data['PULSE'].values)
x_train = np.expand_dims(x_train,axis=2)

x_train = create_sequences(data['SpO2'].values)
x_train = np.expand_dims(x_train,axis=2)

x_train = create_sequences(data['ABPDias'].values)
x_train = np.expand_dims(x_train,axis=2)

x_train = create_sequences(data['ABPMean'].values)
x_train = np.expand_dims(x_train,axis=2)

x_train = create_sequences(data['RESP'].values)
x_train = np.expand_dims(x_train,axis=2)

and here is my model is training

model = Sequential()
model.add(Conv1D(filters=32, kernel_size=7, padding="same", strides=2, input_shape=(x_train.shape[1],x_train.shape[2])))
model.add(MaxPooling1D(pool_size=1,padding="valid"))
model.add(Dropout(0.2))
model.add(Conv1D(filters=16, kernel_size=7, padding="same", strides=2))
model.add(LSTM(units=20, return_sequences=True))
model.add(Dropout(0.2))
model.add(Conv1DTranspose(filters=16, kernel_size=7, padding="same",strides=2))
model.add(Conv1D(filters=32, kernel_size=7, padding="same"))
model.add(MaxPooling1D(pool_size=2,padding="valid"))
model.add(Conv1DTranspose(filters=32, kernel_size=7, padding="same",strides=4,activation="relu"))
model.add(Conv1DTranspose(filters=1, kernel_size=7, padding="same"))

model.compile(optimizer="adam", loss="mse")

model.summary()



history = model.fit(
 x_train,
 x_train,
 epochs=150,
 batch_size=128,
 validation_split=0.1
)

But this took a lot of time, what I am missing?, can anyone guide me And one thing more is, should I use train_test_split for unlabled data?

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