A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting

Stefani, Jacopo De; Caelen, Olivier; Hattab, Dalila; Borgne, Yann‐Aël Le; Bontempi, Gianluca

doi:10.1007/978-3-030-13463-1_1

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…RELATED WORK The analysis performed utilized multivariate and multi-step machine learning algorithms [30] for cryptocurrency volatility forecasting, this research highlighted that even for relatively small datasets the calculation time for a successful prediction was comparatively much higher than for any other comparable algorithm. The usage [1,9,4] of multivariate linear regression for predicting cryptocurrency prices was proposed which focused on multivariate linear regression, using the historic dataset for training, and then trying to compare future values for validation of the model created.…”

Section: IImentioning

confidence: 99%

Digital Currency Price Prediction using Machine Learning

Kadadekar,

Shaikh,

Shahir

2023

IJRASET

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Coming into the 21st century, the views towards finance and investments have drastically shifted from traditional assets like gold, land, and property to assets in the digital domain, like investing in various stocks, mutual funds, currencies, etc. The more recent developments of technology and newer investment options have brought various block-chained digital currencies to the forefront. Digital currency options offer massive gains but are very dynamic and quick-moving investments, making predicting the future values for maximum gains difficult, thereby reducing the confidence of even seasoned investors. While digital currencies saw massive gains in the past decade, research in deep learning has seen equivalent growth in terms of efficiency, required computation, and prediction rate. In this project, we explored various digital currencies, using multiple machine learning models from extended trees to time series analysis, only to use Long Short-Term Memory (LSTM) model to get accurate predictions for the tested parameters. While there is a risk of overfitting the dataset, given that this project uses APIs to get real-time data, the risk is mitigated by the fact that it’s giving precise outputs for unseen data.

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Section: IImentioning

confidence: 99%

Digital Currency Price Prediction using Machine Learning

Kadadekar,

Shaikh,

Shahir

2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…Machine Learning is a subset of artificial intelligence concerned with creating algorithms that can modify themselves without human intervention to produce the desired result. Machine Learning Algorithm requires structured data for proper operation [4]. Each ML project has a set of specific factors that affect the size of the AI training datasets needed for successful modeling.…”

Section: прогнозування фінансової волатильності за допомогою методу г...mentioning

confidence: 99%