信息技术尤其是互联网的快速发展，为用户提供了快捷简便的信息获取途径。随着互联网服务带来的这些便利，许多原本投资黄金和贵金属的个人如今转向以加密货币形式进行数字投资。然而，加密货币投资充满不确定性，且每日都存在波动。这种风险给加密货币投资者带来了重大挑战，可能导致巨额投资损失。加密货币的价值不确定性是加密货币投资领域的关键问题。预测是用于预测这些加密货币未来价值的方法之一。研究利用长短期记忆网络（Long Short Term Memory，LSTM）、支持向量机（Support Vector Machine，SVM）和多项式回归算法模型进行预测，并开展性能对比以确定哪种算法模型最适合预测加密货币价格。研究采用均方误差作为对比基准。通过应用这三个构建好的算法模型发现，采用线性核的支持向量机产生的均方误差比长短期记忆网络和多项式回归算法模型更小，其均方误差值为0.02。
英文摘要：
The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02. Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error, Polynomial Regression, Support Vector Machine
Miami Dade College（迈阿密戴德学院）；University of Virginia（弗吉尼亚大学）；Florida A&M University（佛罗里达农工大学）
In this paper we apply neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price. This paper's code contains Jupyter notebooks, one of which outputs a timeseries graph of any cryptocurrency price once a CSV file of the historical data is inputted into the program. Another Jupyter notebook trains an LSTM, or a long short-term memory model, to predict a cryptocurrency's closing price. The LSTM is fed the close price, which is the price that the currency has at the end of the day, so it can learn f…
Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of Notre Dame（圣母大学）
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with …
We investigate anomaly detection in an unsupervised framework and introduce Long Short Term Memory (LSTM) neural network based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM based structure and obtain fixed length sequences. We then find a decision function for our anomaly detectors based on the One Class Support Vector Machines (OC-SVM) and Support Vector Data Description (SVDD) algorithms. As the first time in the literature, we jointly train and optimize the parameters of the LSTM architecture and the OC-SVM (or SVDD) algorith…
International Islamic University Malaysia（马来西亚国际伊斯兰大学）
Bitcoin is the first digital decentralized cryptocurrency that has shown a significant increase in market capitalization in recent years. The objective of this paper is to determine the predictable price direction of Bitcoin in USD by machine learning techniques and sentiment analysis. Twitter and Reddit have attracted a great deal of attention from researchers to study public sentiment. We have applied sentiment analysis and supervised machine learning principles to the extracted tweets from Twitter and Reddit posts, and we analyze the correlation between bitcoin price movements and sentiment…
COMSATS University Islamabad, Abbottabad Campus（COMSATS大学伊斯兰堡分校阿伯塔巴德校区）
This study presents a novel method to recognize human physical activities using CNN followed by LSTM. Achieving high accuracy by traditional machine learning algorithms, (such as SVM, KNN and random forest method) is a challenging task because the data acquired from the wearable sensors like accelerometer and gyroscope is a time-series data. So, to achieve high accuracy, we propose a multi-head CNN model comprising of three CNNs to extract features for the data acquired from different sensors and all three CNNs are then merged, which are followed by an LSTM layer and a dense layer. The configu…
Northeast Electric Power University（东北电力大学）；Hanyang University（汉阳大学）
The emerging cryptocurrency market has lately received great attention for asset allocation due to its decentralization uniqueness. However, its volatility and brand new trading mode have made it challenging to devising an acceptable automatically-generating strategy. This study proposes a framework for automatic high-frequency bitcoin transactions based on a deep reinforcement learning algorithm-proximal policy optimization (PPO). The framework creatively regards the transaction process as actions, returns as awards and prices as states to align with the idea of reinforcement learning. It com…
COMSATS University Islamabad（COMSATS伊斯兰堡大学）；Tsinghua University（清华大学）；Arab Open University-Palestine（阿拉伯开放大学巴勒斯坦分校）；Jashore University of Science and Technology（杰索尔科技大学）
Approximately, 50 million people in the world are affected by epilepsy. For patients, the anti-epileptic drugs are not always useful and these drugs may have undesired side effects on a patient's health. If the seizure is predicted the patients will have enough time to take preventive measures. The purpose of this work is to investigate the application of bidirectional LSTM for seizure prediction. In this paper, we trained EEG data from canines on a double Bidirectional LSTM layer followed by a fully connected layer. The data was provided in the form of a Kaggle competition by American Epileps…
Department of Mathematics；Statistics, Texas A\&M University - Corpus Christ, 6300 Ocean Dr., Corpus Christi, TX 78412, USA
Financial markets are highly complex and volatile; thus, learning about such markets for the sake of making predictions is vital to make early alerts about crashes and subsequent recoveries. People have been using learning tools from diverse fields such as financial mathematics and machine learning in the attempt of making trustworthy predictions on such markets. However, the accuracy of such techniques had not been adequate until artificial neural network (ANN) frameworks were developed. Moreover, making accurate real-time predictions of financial time series is highly subjective to the ANN a…
New York University Leonard N. Stern School of Business（纽约大学斯特恩商学院）
This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predicts price movement of cryptocurrencies from trade-by-trade data. The main focus of this study is on predicting short-term price changes in a fixed time horizon from a looking back period. By carefully designing features and detailed searching for best hyper-parameters, the model is trained to achieve high performance on nearly a year of trade-by-trade data. The optimal model delivers stable high performance(over 60% accuracy) on out-of-sample test periods. In a realistic trading simulation set…
Charles Sturt University（查尔斯特大学）；Feni University（费尼大学）；Z.H. Sikder University of Science and Technology（Z.H. 锡克德科技大学）；Macquarie University（麦考瑞大学）
The combination of big data and deep learning is a world-shattering technology that can greatly impact any objective if used properly. With the availability of a large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped to predict the future trend of any health problems. From the literature survey, we found the SVM was used to predict the heart failure rate without relating objective factors. Utilizing the intensity of important historical information in electronic health records (EHR), we have built a smart and predictive model utilizing …
San José State University（圣何塞州立大学）；Department of Computer Science, San José State University（圣何塞州立大学计算机科学系）；San José, California, USA（美国加利福尼亚州圣何塞）
We report on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin. Bitcoin is the leading cryptocurrency and has the highest market capitalization among digital currencies. Predicting Bitcoin values may help understand and predict potential market movement and future growth of the technology. Unlike (mostly) repeating phenomena like weather, cryptocurrency values do not follow a repeating pattern and mere past value of Bitcoin does not reveal any secret of future Bitcoin value. Humans follow general sentiments and technical analysis to invest in th…
Comparative Analysis of Predicting Subsequent Steps in Hénon Map
School of Digital Sciences, Digital University Kerala（喀拉拉邦数字大学数字科学学院）；SUPA, School of Physics and Astronomy, The University of Edinburgh（爱丁堡大学SUPA物理与天文学院）
Wenzhou University（温州大学）；Wenzhou Polytechnic（温州职业技术学院）；National Yunlin University of Science and Technology（云林科技大学）；Hong Kong Polytechnic University（香港理工大学）；Obuda University（奥布达大学）；Slovak University of Technology in Bratislava（斯洛伐克布拉迪斯拉发工业大学）
Streamlined weirs which are a nature-inspired type of weir have gained tremendous attention among hydraulic engineers, mainly owing to their established performance with high discharge coefficients. Computational fluid dynamics (CFD) is considered as a robust tool to predict the discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modeling techniques, as an alternative to CFD simulation, to predict the discharge coefficient based on an experimental dataset. To this end, after splitting the dataset using a k fold cross validatio…
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland（数据分析洞察中心、计算学院、都柏林城市大学，都柏林，爱尔兰）；Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam（信息技术学院、胡志明市开放大学，胡志明市，越南）；Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, Vietnam（电气与电子工程学院、胡志明市交通大学，胡志明市，越南）；School of Public Health, University College Cork, Cork, Ireland（公共卫生学院、科克大学学院，科克，爱尔兰）
