1. | Set the neural network (NN) topology by assigning l |

2. | Chose the data for truth test that are just one or two of the last time series data that will not be used by training or validating processes. |

3. | Train the NN with 85% of the data and check the overfitting with the remaining 15%. |

4. | Stop the training if the generalization error increases. |

5. | If the truth test data is not suitably forecasted, go to 1. |

6. | Set H = 0.5, set θ via Equation (11). |

7. | Run Monte Carlo including H described by Equation (10). |

8. | Select the mean value that gives the best match of H according to the long or the short range scenario. |

9. | The obtained mean series generated and its variance are the algorithm results. |

10. | Given the case that the results were not credible, go to 6 and modify the noise characteristic by changing H, 0 < H < 1. |