# Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

arXiv: https://arxiv.org/abs/1802.04065 · HTML: https://mlsanity.com/paper/1802.04065/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data](https://mlsanity.com/paper/2110.14317/) (2021)
- [Review of deep learning models for crypto price prediction: implementation and evaluation](https://mlsanity.com/paper/2405.11431/) (2024)
- [MountainLion: A Multi-Modal LLM-Based Agent System for Interpretable and Adaptive Financial Trading](https://mlsanity.com/paper/2507.20474/) (2025)
- [TCGAN: Convolutional Generative Adversarial Network for Time Series Classification and Clustering](https://mlsanity.com/paper/2309.04732/) (2023)
- [A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind Prediction](https://mlsanity.com/paper/2309.04733/) (2023)
- [A Data-driven Deep Learning Approach for Bitcoin Price Forecasting](https://mlsanity.com/paper/2311.06280/) (2023)

## Abstract (arXiv)

In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experiments are performed to evaluate a variety of statistical and machine learning approaches.