Bitcoin Prediction and Time Series Analysis

Chakravarty, Krishna; Pandey, Manjusha; Routaray, Siddharth

doi:10.1007/978-3-030-19562-5_39

Cited by 3 publications

(1 citation statement)

References 8 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. However, after optimizing the machine learning models to fit multiple features mentioned above, the resultant accuracy hit its peak at 67 percent for the prediction of BTC price.…”

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%