In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

The advancement of artificial intelligence (AI) experiences massive variations for years in which numerous effective applications have been provided to the general public to offer a very comfortable life at present [

Currently, a brand-new internet economics structure established by world impacts namely digital currency, peer-to-peer (P2P), blockchain (BC), and crowdfunding, might act as a majority portion in the expansion of the global monetary markets [

Ji et al. [

This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model to predict the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE approach was executed and the results are inspected in many aspects. In short, the key contribution of the study is listed as follows.

Design a new ELMAE model to predict the return rate of financial products.

Apply JSO algorithm is exploited to tune the parameters related to the ELMAE model.

Employ JSO technique for optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.

Validate the performance of the proposed model on Ethereum (ETH) return rate and investigate the results under several measures.

In this study, a new JSO-ELMAE algorithm was introduced for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model to predict the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.

Primarily, the presented JSO-ELMAE model designs a new ELMAE model to predict the return rate of financial products. At this time, ELMAE was regarded as a classifier method. It executes attained features and evaluates the probability for objects present in the image. Mostly, the activation function and dropout layer are utilized to establish non-linearity and decrease over-fitting problems correspondingly. The ELM has been determined as single hidden-layer feed-forward neural network (SLFN). It can be obvious that hidden layer is non-linear because of the occurrence of nonlinear activation function [

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The ELM has been upgraded as kernel-based ELM (KELM) with kernel trick. Assume

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In this work, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results [

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The jellyfish moves inside the swarm or towards the ocean current. The transitions between them are guided through a timing control system (TCS) [

Next, when the food supply is sufficient, the jellyfish is attracted to the corresponding position.

Then, the objective values display the food quantity. A time regulation parameter

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For verifying the goodness of the presented JSO-ELMAE method, the Ethereum (ETH) return rate is selected as target and the experimental analysis is executed on it for verifying the predictive outcomes on the time series. The comparative study is made with recent models under several measures.

Annualized rate of bitcoin | ||||
---|---|---|---|---|

Sequence of test samples | Actual | Predicted | ||

Run-1 | Run-2 | Run-3 | ||

1 | 2.718 | 2.838 | 2.718 | 2.728 |

10 | 2.828 | 2.688 | 2.818 | 2.938 |

20 | 3.179 | 3.179 | 3.059 | 3.069 |

30 | 2.837 | 2.747 | 2.887 | 2.957 |

40 | 2.613 | 2.723 | 2.423 | 2.523 |

50 | 2.818 | 2.898 | 2.988 | 2.858 |

60 | 3.177 | 3.317 | 3.207 | 3.167 |

70 | 3.175 | 3.265 | 3.125 | 3.225 |

80 | 3.072 | 2.872 | 3.042 | 3.192 |

90 | 2.616 | 2.566 | 2.706 | 2.546 |

100 | 2.953 | 3.103 | 3.103 | 2.803 |

Additionally, on test sample 60 and actual value of 3.177, the JSO-ELMAE algorithm has predicted the value of 3.167. Then, on test sample 70 and actual value of 3.175, the JSO-ELMAE algorithm has predicted the value of 3.225. On the other hand, on test sample 80 and actual value of 3.072, the JSO-ELMAE algorithm has predicted the value of 3.192. In line with, on test sample 90 and actual value of 2.616, the JSO-ELMAE algorithm has predicted the value of 2.546. Eventually, on test sample 100 and actual value of 2.953, the JSO-ELMAE system predicted the value of 2.803. Thus, it is apparent that the JSO-ELMAE model has obtained closer predictive outcomes over other models.

Methods | Training set | Testing set | ||||
---|---|---|---|---|---|---|

MSE | RMSE | MAPE | MSE | RMSE | MAPE | |

JSO-ELMAE | 0.0129 | 0.1136 | 2.8614 | 0.0244 | 0.1562 | 3.7892 |

OLS-SVM | 0.0655 | 0.2559 | 3.1593 | 0.0802 | 0.2832 | 4.2226 |

PSO-LSSVR | 0.0601 | 0.2452 | 3.1446 | 0.0903 | 0.3005 | 4.4421 |

SVM | 0.1181 | 0.3437 | 4.6897 | 0.1172 | 0.3423 | 4.5171 |

BPNN | 0.0752 | 0.2742 | 3.1656 | 0.1021 | 0.3195 | 4.7812 |

GA-SVM | 0.0955 | 0.3090 | 4.5197 | 0.1012 | 0.3181 | 4.7338 |

ANN | 0.1068 | 0.3268 | 4.5150 | 0.1006 | 0.3172 | 4.7889 |

Therefore, the experimental results assured the supremacy of the JSO-ELMAE model over recent models. It can be employed for reliable and robust forecasting the return rate of BC financial products in real time environments.

In this study, a novel JSO-ELMAE algorithm was introduced for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE algorithm was executed and the results are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches. In future, DL models are included to raise the predictive outcomes of the ELMAE algorithm.