[Python] How to predict list elements outside the bounds of a py dataframe?

I built a model based on LSTM and trained it to predict stock price changes during the day, where the unit of time is one second. The test data gives a result comparable to the real one, but I need to get a forecast for the future outside the existing range. What am I doing wrong?


close = df['close']
values = close.values
values = values.reshape(-1, 1)

training_scaler = MinMaxScaler(feature_range=(0, 1))

testing_input = values
testing_input = training_scaler.fit_transform(testing_input)
testing = []
for i in range(50, len(testing_input) + 50):
testing.append(testing_input[i - 50:i][0])

testing = np.array(testing)
testing = np.reshape(testing, (testing.shape[0], testing.shape[1], 1))
predict = model.predict(testing)
predict = training_scaler.inverse_transform(predict)

plt.plot(values, color='blue', label='Stock Price')
plt.plot(predict, color='red', label='Predicted Stock Price')
plt.title('Changes')
plt.xlabel('Timeline')
plt.ylabel('Stock Price')
plt.legend()
plt.show()


My results

It turns out that the model predicts data that I already know. How can I predict future data?

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