A novel cryptocurrency price trend forecasting model based on LightGBM

Sun, Xiaolei; Liu, Mingxi; Sima, Zeqian

doi:10.1016/j.frl.2018.12.032

Cited by 302 publications

(127 citation statements)

References 11 publications

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“…However, they have forecasted the price trend (falling, not falling) by combining daily data of 42 kinds of cryptocurrencies with key economy indicators, but we have predicted and forecasted the price of 9 cryptocurrencies and cci30. In paper [15], only six months daily trading data from January 1, 2018 to June 30, 2018 were collected and considered for forecasting, but in our paper, we have taken yearly data, as a result of which our result is slightly less than in [15].…”

Section: Comparison With Previous Resultsmentioning

confidence: 98%

“…Secondly, it is a very scalable machine learning model due to its construction process and finally, it is also a rule-based learning method [26]. A number of works dealing with prediction and forecasting of sales as well as cryptocurrency prices in the literature have successfully employed gradient boosted trees model [12,15,27]. Gradient boosted trees model is an ensemble of either regression or classification tree models, which is a forward learning ensemble method that obtains predictive results through gradually improved estimations.…”

Section: Predictive Model: Gradient Boosted Treesmentioning

confidence: 99%

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An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Chowdhury

Rahman

et al. 2020

Physica A: Statistical Mechanics and its Applications

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At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.

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Section: Comparison With Previous Resultsmentioning

confidence: 98%

Section: Predictive Model: Gradient Boosted Treesmentioning

confidence: 99%

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Chowdhury

Rahman

et al. 2020

Physica A: Statistical Mechanics and its Applications

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“…The SVR model was selected because it produced the best prediction performance of the various ML techniques when retrieving the mangrove AGB in a coastal area in North Vietnam [8]; the RF model was selected because it outperformed other techniques for mapping and modeling the mangrove AGB change in South Vietnam [22]. The other GBDT models represent the most widely used methods for robust regression problems in several fields, as reported in recent studies [32,64,65]. In the current study, we determined the optimal hyperparameters of each ML algorithm using the grid search with a 5-fold CV in the Python environment.…”

Section: Machine Learning Models Usedmentioning

confidence: 99%

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

et al. 2020

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This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R 2 ) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R 2 = 0.683, RMSE = 25.08 Mg·ha −1 ) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha −1 to 142 Mg·ha −1 (with an average of 72.47 Mg·ha −1 ). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

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“…Besides, Li and Wang explored the Bitcoin exchange rate determination by taking into consideration economic factors such as money supply, GDP, interest rate and inflation [9]. Other key economic indicators such as the US S&P 500 index, the US dollar index futures, the RMB to US dollar Exchange rate, Stock Composite Index, the RMB to U.S. dollar exchange rate, the FTSE China A50 and the WTI crude oil are simultaneous available to the price fluctuation of cryptocurrencies [10].…”

Section: Introductionmentioning

confidence: 99%

“…Eventually, in the short term, online factors may not be the best predictors, while they tend to show a stronger and stable relationship in the long run. In addition, It is found that Google's page views of relevant information can also increase the price forecasting ability of cryptocurrencies [10,15].…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency Price Prediction based on Multiple Market Sentiment

Wang¹,

Chen²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult.To this end, based on the transaction data of three different markets and the number and content of user comments and responses from online forums, this paper constructs a price prediction model of cryptocurrencies using a variety of machine learning and deep learning algorithms. It turns out that the trading price premium rate in different markets will affect the price to be predicted, and adding social media comment features can significantly improve the accuracy of the forecast. This article is conducive to investors who encrypt currencies to make more scientific decisions.

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A novel cryptocurrency price trend forecasting model based on LightGBM

Cited by 302 publications

References 11 publications

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

Cryptocurrency Price Prediction based on Multiple Market Sentiment

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