Bitcoin price forecasting method based on CNN‐LSTM hybrid neural network model

Li, Yan; Dai, Wei

doi:10.1049/joe.2019.1203

Cited by 51 publications

(25 citation statements)

References 7 publications

(7 reference statements)

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“…Their empirical outcomes ensured the importance of an ensemble model based on data input structure [27]. Regarding these prior works, the ensemble method combining two or more forecasting models could be essential to improve the forecasting performance in many areas, such as forecasting of the daily average number of COVID-19 patients, bitcoin price forecasting, household load forecasting, and typhoon formation forecasting [28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Phyo

Byun

2021

Symmetry

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The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

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Section: Related Workmentioning

confidence: 99%

Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Phyo

Byun

2021

Symmetry

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“…Considering the potential, deep learning has been applied in various domains and applications for different purposes [ Many researchers exploited the convolutional aspect of CNN in combination with LSTM to improve the performance of time-series prediction/forecasting in various applications, such as for inventory prediction [30], stock price prediction [63] [64] [65] [66], gold price forecasting [28], Bitcoin price forecasting [67], tourist flow forecasting [68], sentiment prediction of social media users [69], household power consumption prediction [70] [27], photovoltaic power prediction [71], wind power forecasting [72], PM2.5 prediction [73] [74], predicting NOx emission in processing of heavy oil [75], forecasting natural gas price and movement [29], urban expansion prediction [76], predicting waterworks operations at a water purification plant [77], predicting sea surface temperature [78], typhoon formation forecasting [79], crop yield prediction [80], COVID-19 detection and predictions [81] [82] [83], human age estimation [84], and so on. [106] used LSTM to predict the availability of mobile edge computing-enabled base stations depending on the vehicle's mobility for offloading the computation jobs from the vehicle to the base station.…”

Section: B Deep Learning For Resource Management and Predictionmentioning

confidence: 99%

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

et al. 2021

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Mobile crowd computing (MCC) that utilizes public-owned (crowd's) smart mobile devices (SMDs) collectively can give adequate computing power without any additional financial and ecological cost. However, the major challenge is to cope with the mobility (or availability) issue of SMDs. User's unpredicted mobility makes the SMDs really unstable resources. Selecting such erratic resources for job schedule would result in frequent job offloading and, in the worst case, job loss, which would affect the overall performance and the quality of service of MCC. In a Local MCC, generally, a set of users are available for a certain period regularly. Based on this information, the chances of a user being available for a certain duration from a given point of time can be predicted. In this paper, we provide an effective model to predict the availability of the users (i.e., their SMDs) in such an MCC environment. We argue that before submitting a job to an SMD, the stability of it is to be assessed for the duration of execution of the job to be assigned. If the predicted availability time is greater than the job size, then only the job should be assigned to the SMD. An accurate prediction will minimize the unnecessary job offloading or job loss due to the early departure of the designated SMD. We propose an advanced convolutional feature extraction mechanism that is applied to LSTM and GRU-based time-series prediction models for predicting SMD availability. To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. We compared the prediction performances of convolutional LSTM and GRU with the basic LSTM and GRU and ARIMA in terms of MAE, RMSE, R 2 , accuracy, and perplexity. In all the measurements, the proposed convolutional LSTM exhibits considerably better prediction performance.

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“…When the literature is carefully reviewed, it was seen that convolutional networks are rarely used for the Bitcoin price prediction. In the study of Li and Dai, hybrid network model combining CNN and LSTM is proposed (Li & Dai, 2020). Their results show that the hybrid model improves the accuracy compared with a single model.…”

Section: Literature Reviewmentioning

confidence: 99%

Günlük Bitcoin Değerini Tahmin Etmek İçin İstatistiksel ve Makine Öğrenimi Algoritmalarının Karşılaştırılması

Aygun¹,

Gunay²

2021

European Journal of Science and Technology

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Increasing fluctuations in pricing and having great profit potential, utilization in advanced machine learning technologies to make robust predictions of cryptocurrencies especially bitcoin have attracted great attention in recent years. In this study, various statistical techniques; Moving Average Analysis and Autoregressive Integrated Moving Average and machine learning (ML) techniques; Artificial Neural Network, Recurrent Neural Network (RNN) and Convolutional Neural Network have been conducted and compared to predict the future value of Bitcoin cryptocurrency price. They have been applied for the univariate time series analysis with a window size of 32. To prove the usefulness of ML algorithms, and to show that the results of RNN is a better, mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) indicators have been applied. The study revealed that recurrent neural network yields better results than other methods in predicting daily Bitcoin price in terms of MSE, MAE and MAPE metrics. Besides, Wilcoxon-Mann-Whitney nonparametric statistic test is applied to test the performance between ARIMA and machine learning algorithms.

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Bitcoin price forecasting method based on CNN‐LSTM hybrid neural network model

Cited by 51 publications

References 7 publications

Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

Günlük Bitcoin Değerini Tahmin Etmek İçin İstatistiksel ve Makine Öğrenimi Algoritmalarının Karşılaştırılması

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