Prediction of Ethereum gas prices using DeepAR and probabilistic forecasting

Ferenczi, Andras; Bădică, Costin

doi:10.1080/24751839.2023.2250113

Cited by 1 publication

(1 citation statement)

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“…Early studies primarily relied on historical price data, but with the evolution of the digital currency landscape, multiple data sources have been employed to enhance prediction accuracy. For instance, recent research has explored the use of off-chain data such as cryptocurrency price streams for Ethereum gas price prediction [19].…”

Section: Related Workmentioning

confidence: 99%

Enhanced Bitcoin Price Direction Forecasting With DQN

Muminov,

Sattarov,

2024

IEEE Access

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In the Bitcoin trading landscape, predicting price movements is paramount. Our study focuses on identifying the key factors influencing these price fluctuations. Utilizing the Pearson correlation method, we extract essential data points from a comprehensive set of 14 data features. We consider historical Bitcoin prices, representing past market behavior; trading volumes, which highlight the level of trading activity; network metrics that provide insights into Bitcoin's blockchain operations; and social indicators: analyzed sentiments from Twitter, tracked Bitcoin-related search trends on Google and on Twitter. These social indicators give us a more nuanced understanding of the digital community's sentiment and interest levels. With this curated data, we forge ahead in developing a predictive model using Deep Q-Network (DQN). A defining aspect of our model is its innovative reward function, tailored for enhancing predicting Bitcoin price direction, distinguished by its multi-faceted reward function. This function is a blend of several critical factors: it rewards prediction accuracy, incorporates confidence scaling, applies an escalating penalty for consecutive incorrect predictions, and includes a time-based discounting to prioritize recent market trends. This composite approach ensures that the model's performance is not only precise in its immediate predictions but also adaptable and responsive to the evolving patterns of the cryptocurrency market. Notably, in our tests, our model achieved an impressive F1-score of 95%, offering substantial promise for traders and investors.

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Section: Related Workmentioning

confidence: 99%