Follow blockchain research across journals, conferences, and preprint repositories.
128 results · page 4 of 6
Rohith K. Ramakrishnan, Anirudh Vadakedath, Anirudh Bhaskar, S. Sachin Kumar · 5 authors
No abstract is available for this record.
Chengge Wen, Siyan Lu, Jiaxuan Jiang
Maximizing returns has always been people's investment goal. Gold and bitcoin are popular with investors because of their hedges and volatility. However, markets are risky and can be influenced by different economic, political and environmental factors. As a result, bitcoin and gold prices fluctuate wildly, leading to uncertain investment and uncertain returns. In order to maximize the profit, this paper completes the data processing and model construction to make decisions. Based on the Markov decision process of avoiding risk avoidance, reducing transaction cost and maintaining liquidity, and assuming that the stock market is not affected by enhanced trading agent, deep reinforcement learning (DRL) is used to simulate stock trading. The application of the model is helpful to forecast the return of investment portfolio and brings strong application value to the relevant practitioners.
Valeri Mladenov, Veselin Chobanov, Thong Vu Van, Pencho Zlatev
The architecture, characteristics, and elements of Blockchain technology used to create a peer-to-peer energy trading platform are outlined in the paper. Here, we discussed the benefits of Blockchain and distributed ledger technology (DLT) for energy trading applications and how they assist the expanding decentralization and democratization ideologies in the energy industry.
Sidi Rong, Tianyou Sun, Dai Hui
In this paper, we preprocessed the data for outliers and added the prices of gold and bitcoin to the training. At the same time, based on the price of the day, we also established a strategy model based on the Sharpe ratio and particle swarm algorithm. We established a comprehensive planning model for cash, gold, and Bitcoin through the planning process. A particle swarm algorithm simulated the optimization process, and the optimal solution was found. Finally, through the sensitivity analysis, we found that as the transaction fee increases, the number of transactions of gold and Bitcoin decreases significantly, and the value decreases.
Zhongqi Miao, Wenxuan Huang
Aiming at the portfolio problem of gold and bitcoin with a given linear trading commission, this paper puts forward the stage implementation forecast and optimal portfolio model. In the aspect of data prediction, SMA is used to predict the initial data, LSTM is used to predict the price trend of long-term data, and daily updated real-time price data is predicted. Considering the risk aversion of investors, the heuristic algorithm is used to solve the daily trading strategy of maximizing utility from September 12th, 2016 to September 12th, 2021. The simulation analysis of the sliding window shows that the algorithm can realize reasonable prediction, which verifies the effectiveness of the algorithm.
Muhammad Nazrin Farhan Nasarudin, Ahmad Ihsan Mohd Yassin, Megat Syahirul Amin Megat Ali, Mohd Khairil Adzhar Mahmood · 6 authors
Bitcoin is a decentralized digital currency that enables people to exchange value without requiring a third-party intermediary. Due to its many advantages, it has received much interest from institutional and individual investors. Despite its meteoric increase, the price of Bitcoin extremely volatile asset class as it purely relies on supply and demand. This presents an interesting opportunity to create a forecasting model. However, many research papers in this area does not analyse the residuals as part of the forecasting resulting in potentially biased models. In this paper, we demonstrate System Identification (SI) residual analysis techniques to the analysis of our forecasting model. The Multi-Layer Perceptron (MLP) Nonlinear Autoregressive with Exogeneous Inputs (NARX) uses historical price data and several technical indicators to predict the future price movements of Bitcoin. The Particle Swarm Optimization (PSO) algorithm was used to find optimal parameters for the model. The model was able to predict one day ahead price in the prediction test. The model has successfully captured the dynamics of the data through the tests performed on residuals. It is also proving the randomness of residuals, albeit some minor violations.
R Marriammal, Reni Hena Helen R, M Rubika, T Sowbhagya
Bitcoin, the king of cryptocurrencies, is central to blockchain technology. A fixed amount of bitcoins is required for each transaction stored in the blockchain. The price of bitcoins fluctuates wildly and is unaffected by any company or marketing techniques, creating both curiosity and terror in the minds of traders. It is possible for consumers to study and invest in bitcoin by anticipating the bitcoin price, which promotes the use of digital money. As a result, a high-prediction-rate prediction model is required. The goal of this project is to employ a variety of machine learning models to predict the price of bitcoin. The best model for predicting bitcoin value is given based on the error percentage of these machine learning algorithms.
Md Mahraj Murshalin Al Moti, Rafsan Shartaj Uddin, Md. Abdul Hai, Tanzim Bin Saleh · 7 authors
A smart grid is an intelligent electricity network that allows efficient electricity distribution from the source to consumers through telecommunication technology. The legacy smart grid follows the centralized oligopoly marketplace for electricity trading. This research proposes a blockchain-based electricity marketplace for the smart grid environment to introduce a decentralized ledger in the electricity market for enabling trust and traceability among the stakeholders. The electricity prices in the smart grid are dynamic in nature. Therefore, price forecasting in smart grids has paramount importance for the service providers to ensure service level agreement and also to maximize profit. This research introduced a Stackelberg model-based dynamic retail price forecasting of electricity in a smart grid. The Stackelberg model considered two-stage pricing between electricity producers to retailers and retailers to customers. To enable adaptive and dynamic price forecasting, reinforcement learning is used. Reinforcement learning provides an optimal price forecasting strategy through the online learning process. The use of blockchain will connect the service providers and consumers in a more secure transaction environment. It will help tackle the centralized system’s vulnerability by performing transactions through customers’ smart contracts. Thus, the integration of blockchain will not only make the smart grid system more secure, but also price forecasting with reinforcement learning will make it more optimized and scalable.
Xinchen Zhang, Linghao Zhang, Qincheng Zhou, Xu Jin
As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.
YUTENG LIU, YUXUAN TIAN, Tianxing Zhou, HONGZHOU WANG
Since the rise of Data Analysis, forecasting of price markets has never stopped and there are numerous forecasting methods, but most of them are only for a single price data.We have chosen bitcoin and gold as the subjects of our study, addressing the multi-objective related prediction problem, explores the volatility relationship between gold and bitcoin to improve its forecasting accuracy, and in doing so, we establishes multiple prediction models,and determines the relationship between prediction accuracy and prediction range.
Ziqiang Tang, Hongping Xie, Changqing Du, Yinying Liu · 6 authors
Peer-to-peer electricity transaction is predicted to play a substantial role in research into future power infrastructures as energy consumption in intelligent microgrids increases. However, the on-demand usage of Energy is a major issue for families to obtain the best cost. This article provides a machine learning predictive power trading framework for supporting distributed power resources in real-time, day-to-day monitoring, and generating schedules. Furthermore, the energy optimization algorithm used in machine learning (EOA-ML) is proposed in this article. The machine learning-based platform suggested two modules: fuel trading and intelligent contracts based on machine learning implemented predictive analytical components. The Blockchain module enables peers to track energy use in real-time, manage electricity trading, model rewards, and irreversible transaction records of electricity trading. A predictive analysis component based on previous power usage data is designed to anticipate short-term energy usage in the Intelligent Contracts. This study utilizes data from the provincial Jeju, Korea’s electricity department on true energy utilization. This study seeks to establish optimal electricity flow and crowdsourced, promoting electricity between consumers and prosumers. Power trading relies on day-to-day, practical environmental control and the planning of decentralized power capitals to satisfy the demands of smart grids. Furthermore, it employs data mining technologies to obtain and study time-series research from the past electricity utilization data. Thus, the time series analytics promotes power controllingto better future efficient planning and managingelectricity supplies. It utilized numerous statistical methods to assess the effectiveness of the suggested prediction model, mean square error in different models of machine learning, recurring neural networks. The efficacy of the proposed system regarding the delay, throughput, and resource using hyperleader caliper is shown. Finally, the suggested approach is successfully applied for power crowdsourcing between prosumer and customer to reach service reliability based on trial findings. The actual and predicted cost analysis has been increased (95%). It minimizes the delay rate to (40.3%) by improving the efficiency rate.
Xiaoxu Du, Zhenpeng Tang, Junchuan Wu, Kaijie Chen · 5 authors
The return series of cryptocurrencies, which are emerging digital assets, exhibit nonstationarity, nonlinearity, and volatility clustering compared to other traditional financial markets, making them exceptionally difficult to forecast. Therefore, accurate cryptocurrency price forecasting is important for both market participants and regulators. It has been demonstrated that improved data forecasting accuracy can be achieved through decomposition, but few researchers have performed information extraction on the residual series generated by data decomposition. Based on the construction of a "decomposition-optimization-integration" hybrid model framework, in this paper, we propose a multi-scale hybrid forecasting model that combines the residual components after primary decomposition for secondary decomposition and integration. This model uses the variational modal decomposition (VMD) method to decompose the original return series into a finite number of components and residual terms; then, the residual terms are decomposed and the features are extracted using the completed ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. The components are predicted by an extreme learning machine optimized by the sparrow search algorithm, and the final predictions are summed to obtain the final results. Forecasts for the returns of Bitcoin and Ethereum, which are major cryptocurrency assets, are compared with other benchmark models constructed based on different ideas, and we find that the proposed quadratic decomposition VMD-Res.-CEEMDAN-SSA-ELM hybrid model demonstrates the optimal and most stable forecasting performance in both one-step and multi-step ahead prediction of the cryptocurrency return series.
Mohamed Salb, Miodrag Živković, Nebojša Bačanin, Amit Chhabra · 5 authors
No abstract is available for this record.
Yeonggwang Kim, Seungmin Oh, Junchurl Yoon, Tai‐Won Um
The NFT (Non-Fungible Token) market has soared in recent years. The concept of NFT originally originated from Ethereum’s token standard and aims to distinguish each token with a distinguishable symbol. Tokens of this type can bind virtual/digital attributes to unique identification items. These features can be used to bind AI learning outcomes to unique identification items. A simple load forecast learning model can be produced with simple PC performance. However, if the structure is complicated or the number of learning epoch increases, the value and importance of the model are made difficult. This study was conducted to promote developer participation in complex model development and to protect works.
ChihYun Chuang, TingFang Lee
On the Ethereum network, it is challenging to determine a gas price that ensures a transaction will be included in a block within a user's required timeline without overpaying. One way of addressing this problem is through the use of gas price oracles that utilize historical block data to recommend gas prices. However, when transaction volumes increase rapidly, these oracles often underestimate or overestimate the price. In this paper, we demonstrate how Gaussian process models can predict the distribution of the minimum price in an upcoming block when transaction volumes are increasing. This is effective because these processes account for time correlations between blocks. We performed an empirical analysis using the Gaussian process model on historical block data and compared the performance with GasStation-Express and Geth gas price oracles. The results suggest that when transactions volumes fluctuate greatly, the Gaussian process model offers a better estimation. Further, we demonstrated that GasStation-Express and Geth can be improved upon by using a smaller training sample size which is properly pre-processed. Based on the results of empirical analysis, we recommended a gas price oracle made up of a hybrid model consisting of both the Gaussian process and GasStation-Express. This oracle provides efficiency, accuracy, and better cost.
Liping Yang
In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.
S. Venkata Lakshmi, N.G. Swadthi, V.Suba Shree, M. Swethamura
Bitcoin, the ruler of cryptocurrency plays an important role in blockchain technology. Every transaction stored in blockchain costs certain amount of bitcoins. The price fluctuation of bitcoins are very unstable and doesn't depend on any business or marketing strategies which builds both interest and fear in the minds of traders. By predicting the bitcoin price it is possible that users can analyze and invest in bitcoin which improves the utilization of digital money. So it is necessary to develop a prediction model with high prediction rate. This work focuses on implementing various machine learning models to predict the price of bitcoin. These machine learning models are evaluated using error percentage from which the best model for predicting bitcoin value is suggested. From the results it is evident that, among the state of the art ML algorithms, LSTM is found to be the best.
Salim Lahmiri, Stelios Bekiros
No abstract is available for this record.
Asit Kumar Das, Debahuti Mishra, Kaberi Das
No abstract is available for this record.
Wei Zhang, Kexin Tao, Junfeng Li, Yanchun Zhu · 5 authors
The interactive information in blockchain architecture establishes an effective communication channel between users and enterprises, enabling them to communicate in a comprehensive and effective manner. Therefore, taking blockchain interactive information as the research object, this paper explores how the intervention of official information on investors affects the stock price movement and then makes predictions on stock prices according to the emotional tendency of interactive information. With the contextual information fusion, a sentiment computing model based on a convolutional neural network is established to extract and quantify the emotional features of blockchain interactive information. Combined with investors’ emotional features, the stock price prediction model based on long short-term memory is proposed. The experiment results show that the accuracy of the model has been improved by incorporating the intervened emotional features, thereby proving that information clarification can have a positive effect on the stock price.
Saeed Nosratabadi, Amirhosein Mosavi, Puhong Duan, Pedram Ghamisi · 9 authors
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.
Waddah Waheeb, Habib Shah, Mohammed Jabreel, Domènec Puig
This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods.
Wenjing Xiao, Chen Liu, Haoquan Wang, Ming Zhou · 7 authors
The traditional natural gas Internet-of-Things (IoT) system has many problems, such as centralized management of resources, noncirculation of data between stations, insecurity of transaction information or account books, and lack of contract consensus. In order to ensure data security and reliable transaction, this article introduces artificial intelligence (AI) and blockchain technology and constructs an AI-enabled and blockchain-powered natural gas IoT system in a smart city. In this article, the natural gas output prediction model based on temporal pattern attention-based LSTMs (TPA-LSTMs) is used to enable the system to sense the change of natural gas deliverability. In addition, we establish a blockchain-based secure natural gas transaction scheme, which dynamically matches the purchase contract and sale contract to maximize the interests of the buyer and the seller and obtain a transaction contract. The experimental results show that our model can predict the output value of natural gas in real time and select the appropriate transaction matching scheme according to the dynamic demand for sales.
Mahdieh Shamsi, Paul Cuffe
This paper proposes and discusses the idea of using nascent blockchain hosted prediction markets as a decentralised crowd sourcing method for renewable energy forecasting. This method is further used as a risk management and hedging tool against volatility in weather variables they depend on. While existing approaches have been centralised by nature, with limited sources of input data and models, prediction markets allow anyone to participate in forecasting by betting on an outcome and earning profits for correct results. Since they have mercenary motivations, these participants are most likely to provide reliable and accurate information. Moreover, renewable energy producers can participate in these prediction markets to hedge against low-income periods due to poor weather conditions. This paper delivers a conceptual framework to exploit prediction markets in a blockchain platform with the aim of forecasting and hedging of renewable energy sources. The potential financial gain from applying this approach has been demonstrated through a case study for a typical small wind power producer.