A novel cryptocurrency price trend forecasting model based on LightGBM

By Xiaolei Sun, Mingxi Liu, Zeqian Sima. 2020

457 citations recorded by the source corpus.

Abstract Forecasting cryptocurrency prices is crucial for investors. In this paper, we adopt a novel Gradient Boosting Decision Tree (GBDT) algorithm, Light Gradient Boosting Machine (LightGBM), to forecast the price trend (falling, or not falling) of cryptocurrency market. In order to utilize market information, we combine the daily data of 42 kinds of primary cryptocurrencies with key economic indicators. Results show that the robustness of the LightGBM model is better than the other methods, and the comprehensive strength of the cryptocurrencies impacts the forecasting performance. This can effectively guide investors in constructing an appropriate cryptocurrency portfolio and mitigate risks.

Subfields: Economics and Econometrics, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/be84492c6090bbbaa932f0be1ef18dc3c3a8e435

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In Economics and Econometrics

In Information Systems

In Management Science and Operations Research

Citations in the corpus

A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks (2023, unknown): “The Lightgbm model is another implementation of the Gradient Boosting Decision Tree algorithm (GBDT), based on two novel techniques: gradient-based one-side sampling and the exclusive feature bundling [31].”
Forecasting gold price with the XGBoost algorithm and SHAP interaction values (2021, unknown): “The LightGBM algorithm built two novel approaches, which are the gradient‑based one‑side sampling and the exclusive feature bundling (Sun et al., 2019).”
Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques (2019, methodology): “Neural network, machine learning and deep learning methods can be used as a single forecasting model in the price [22], volatility [4] and trend [23] forecasting of cryptocurrencies in the financial market as well as they can be also used as a combined forecasting model together with decomposition o”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “LightGBM has also been applied to application domains such as finance [177, 178, 179], health-care [180, 181, 182], and marketing [183, 184].”