CryptoNet: Using Auto-Regressive Multi-Layer Artificial Neural Networks to Predict Financial Time Series
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| Publié dans: | Information vol. 13, no. 11 (2022), p. 524 |
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| Autres auteurs: | , |
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MDPI AG
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| Accès en ligne: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Ranaldi, Leonardo |u Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy; Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Rome, Italy | |
| 245 | 1 | |a Crypto<i>Net</i>: Using Auto-Regressive Multi-Layer Artificial Neural Networks to Predict Financial Time Series | |
| 260 | |b MDPI AG |c 2022 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a When analyzing a financial asset, it is essential to study the trend of its time series. It is also necessary to examine its evolution and activity over time to statistically analyze its possible future behavior. Both retail and institutional investors base their trading strategies on these analyses. One of the most used techniques to study financial time series is to analyze its dynamic structure using auto-regressive models, simple moving average models (SMA), and mixed auto-regressive moving average models (ARMA). These techniques, unfortunately, do not always provide appreciable results both at a statistical level and as the Risk-Reward Ratio (RRR); above all, each system has its pros and cons. In this paper, we present CryptoNet; this system is based on the time series extraction exploiting the vast potential of artificial intelligence (AI) and machine learning (ML). Specifically, we focused on time series trends extraction by developing an artificial neural network, trained and tested on two famous crypto-currencies: Bitcoinand Ether. CryptoNet learning algorithm improved the classic linear regression model up to 31% of MAE (mean absolute error). Results from this work should encourage machine learning techniques in sectors classically reluctant to adopt non-standard approaches. | |
| 653 | |a Stock exchanges | ||
| 653 | |a Machine learning | ||
| 653 | |a Regression analysis | ||
| 653 | |a Trends | ||
| 653 | |a Forecasting | ||
| 653 | |a Multilayers | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Regression models | ||
| 653 | |a Securities markets | ||
| 653 | |a Neural networks | ||
| 653 | |a Volatility | ||
| 653 | |a Digital currencies | ||
| 653 | |a Algorithms | ||
| 653 | |a Autoregressive moving-average models | ||
| 653 | |a Methods | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Time series | ||
| 653 | |a Statistical analysis | ||
| 700 | 1 | |a Gerardi, Marco |u Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy | |
| 700 | 1 | |a Fallucchi, Francesca |u Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy | |
| 773 | 0 | |t Information |g vol. 13, no. 11 (2022), p. 524 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2734629652/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2734629652/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
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