Stock price prediction is currently a research focus in the financial field, especially in blockchain research. The central focus of this research is to forecast Bitcoin's closing price through the integration of deep learning techniques, specifically employing Long Short-Term Memory (LSTM). This study takes into account that Bitcoin is a mainstream virtual currency, and predicting its future price can help investors make better judgments in trading. The goal of this exploration is to identify the most favorable parameter combinations and function prediction applications, ultimately obtaining the most accurate prediction results. The research process includes dataset selection, data processing, model construction, and training. Then adjust and improve the parameters used in the model, and record the process. Finally, test the model and output the test results. And model testing and result output. At the end of the experiment, the effects of different optimizers and parameters on the training results were compared, and the optimal combination was found. The model's predictive accuracy was evaluated through the examination of test data. This study can provide valuable references for researchers and firms.
The modern power generation systems are increasing their reliance on high penetrations of distributed energy resources (DERs). However, the optimal dispatching mechanisms mainly rely on central controls which receive the load demand information from the electricity utility providers and allocate the electricity production targets to participating generating units. The lack of transparency and control over the DER fuel inputs makes the physical power purchase agreements (PPAs) a cumbersome task. This research work proposes an innovative fractal moth flame optimization (FMFO) approach to tackle the problem of integrated load dispatch (ILD). The proposed methodology provides a mechanism to integrate the information of the proposed optimizer, i.e., FMFO into the smart contracts enabled by the blockchain technology. This problem entails the allocation of loads to power-generating units in a manner that minimizes the total generation cost in a decentralized manner. To improve the efficiency of dispatch operations in the presence of a substantial integration of wind energy, this study proposes a novel framework based on the principles of fractal heritage, drawing inspiration from the classical MFO method. To assess the effectiveness and adaptability of the algorithm suggested, various non-convex scenarios in the context of optimization for ILD are considered. These scenarios incorporate valve-point loading effects (VPLEs), capacity limitations, power plants with multiple fuel options, and the presence of stochastic wind (SW) power uncertainty, following a Weibull distribution. The findings demonstrate exceptional performance in terms of minimizing fuel generation costs compared to traditional algorithms.
High Accessibility and Easy Investment makes Cryptocurrency an important income source for many people. Cryptocurrency is a kind of Digital/Virtual currency which is created using blockchain Technology and is protected by Cryptography. Cryptocurrencies enables users to Accept, Transfer and request the capital between the Users without the requirement of intermediaries such as banks. Now a day many Cryptocurrencies are available across the world such as Bitcoin, Litecoin, Monero, Dogecoin etc. This study is more determined over a very famous and demanding Cryptocurrency known as Bitcoin over the past years. Here, firstly we make an effort to predict the price of bitcoin by examining numerous numbers of parameters that affect the cost of bitcoin. Different kinds of Machine learning models will be used to estimate the price of Bitcoin. This study provides the accuracy and precision of each model that are used in this study and determine the suitable method to estimate the price more accurately.
This paper comprehensively examines Large Language Models (LLMs) in sentiment analysis, specifically focusing on financial markets and exploring the correlation between news sentiment and Bitcoin prices. We systematically categorize various LLMs used in financial sentiment analysis, highlighting their unique applications and features. We also investigate the methodologies for effective data collection and categorization, underscoring the need for diverse and comprehensive datasets. Our research features a case study investigating the correlation between news sentiment and Bitcoin prices, utilizing advanced sentiment analysis and financial analysis methods to demonstrate the practical application of LLMs. The findings reveal a modest but discernible correlation between news sentiment and Bitcoin price fluctuations, with historical news patterns showing a more substantial impact on Bitcoin’s longer-term price than immediate news events. This highlights LLMs’ potential in market trend prediction and informed investment decision-making.
In this research, we analyzed three different models for Ethereum price prediction: a custom Hidden Markov Models (HMM), GHMM Pretrained, Deep Reinforcement Learning and LSTM. Our results demonstrate the distinct strengths and weaknesses of every model. Although HMM and HMM Pretrained excel in capturing volatility and short-term price fluctuations, the custom model demonstrates remarkable predictive capabilities for long-term trends. The present study provides significant contributions to the field of cryptocurrency price prediction, hence assisting traders, investors, and scholars in maneuvering through the complex Ethereum market.
Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they properly estimate supply and demand variations and optimize energy distribution, and the other featured values, for participants in decentralized energy marketplaces. Consequently, DeepResTrade is a research work that presents an advanced model for predicting prices in a given traditional energy market. This model includes numerous fundamental components, including the concept of P2P trading systems, long-term and short-term memory (LSTM) networks, decision trees (DT), and Blockchain. DeepResTrade utilized a dataset with 70,084 data points, which included maximum/minimum capacities, as well as renewable generation, and price utilized of the communities. The developed model obtains a significant predictive performance of 0.000636% Mean Absolute Percentage Error (MAPE) and 0.000975% Root Mean Square Percentage Error (RMSPE). DeepResTrade’s performance is demonstrated by its RMSE of 0.016079 and MAE of 0.009125, indicating its capacity to reduce the difference between anticipated and actual prices. The model performs admirably in describing actual price variations in, as shown by a considerable R2 score of 0.999998. Furthermore, F1/recall scores of [1, 1, 1] with a precision of 1, all imply its accuracy.
With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hot spot in the power industry. This paper aims to combine neural network algorithms with blockchain technology to establish a trustworthy and efficient short-term demand forecasting model. By leveraging the distributed ledger and immutability features of blockchain, we ensure the security and reliability of power demand data. Meanwhile, short-term power demand forecasting research using neural networks has the potential to increase the stability of the power system and offer opportunities for improved operations. In this paper, the root mean-square-error model evaluation indicator was used to compare the back propagation (BP) neural network algorithm and the traditional forecasting algorithm. The evaluation was performed on the randomly selected five household power datasets. The results show that, by comparing the long short-term memory network (LSTM) model with the BP neural network model, it was determined that the average prediction impact increases by about 25.7% under stable power demand. The short-term power prediction model of the BP neural network has the average error values more than two times lower than the traditional prediction model. It was shown that the use of the BP neural network algorithm and blockchain could increase the accuracy of short-term power demand forecasting, allowing the neural network-based algorithm to be implemented and taken into account in the research on short-term power demand forecasting.
Mahfuzur Rahman, Solaiman Chowdhury, Mohammad Shorfuzzaman, Mohammad Kamal Hossain · 5 authors
The advancement of mircogrids and the adoption of blockchain technology in the energy-trading sector can build a robust and sustainable energy infrastructure. The decentralization and transparency of blockchain technology have several advantages for data management, security, and trust. In particular, the uses of smart contracts can provide automated transaction in energy trading. Individual entities (household, industries, institutes, etc.) have shown increasing interest in producing power from potential renewable energy sources for their own usage and also in distributing this power to the energy market if possible. The key success in energy trading significantly depends on understanding one’s own energy demand and production capability. For example, the production from a solar panel is highly correlated with the weather condition, and an efficient machine learning model can characterize the relationship to estimate the production at any time. In this article, we propose an architecture for energy trading that uses smart contracts in conjunction with an efficient machine learning algorithm to determine participants’ appropriate energy productions and streamline the auction process. We conducted an analysis on various machine learning models to identify the best suited model to be used with the smart contract in energy trading.
This article reveals a specific category of solutions for the 1+1 variable order (VO) nonlinear fractional Fokker-Planck equations. These solutions are formulated using VO q-Gaussian functions, granting them significant versatility in their application to various real-world systems, such as financial economy areas spanning from conventional stock markets to cryptocurrencies. The VO q-Gaussian functions provide a more robust expression for the distribution function of price returns in real-world systems. Additionally, we analyzed the temporal evolution of the anomalous characteristic exponents derived from our study, which are associated with the long-term (power-law) memory in time series data and autocorrelation patterns.
I Made Gede Abandi Semeru, Yunieta Anny Nainggolan
Abstract : In forming their portfolios, investors should analyze the risk and return of each investment instrument. This is aimed at preventing investors from speculating and gambling with their investments. Conducting an investment portfolio optimization study on LQ-45 stock index, government bond, USD, gold, and Bitcoin can provide valuable insights due to unique market characteristics in Indonesia. This research analyzes the formation of investment instruments over the last 60 months, specifically from January 2018 to December 2022. The research method used in this study is quantitative research aimed at selecting several investment instruments for a portfolio in Indonesia. The portfolio aims to minimize risk and maximize return using the Markowitz method, also known as the optimal portfolio. To fulfill the objectives of this research, data on the prices of each instrument are required. An optimal portfolio can be obtained by combining two instruments: 18% bitcoin and 82% gold. This optimal portfolio can achieve an expected return of 1.29% with a risk level of 5.15%. Considering a risk-free rate of 0.375%, this portfolio forms a slope of 0.1775, which is the largest slope formed between the combination of risk-free instruments and risky portfolios. Investors should allocate their funds more wisely, considering not only the highest return but also the associated risk. High returns often come with high risks, so investors need to assess the risk-return trade-off before making investment decisions.
The long short-term memory (LSTM) network and a cutting-edge method that combines wavelet decomposition and LSTM (W-LSTM) were applied to deep learning in this study's analysis of Bitcoin's price and movement. To be specific, it predicted next day’s both price and price movement (trend) with historical data. The input of the model is close price itself, basic trading information, and technical indicators calculated solely on basic trading information. Large number of numerical experiments come to the same conclusion that: for price prediction, only close price as input obtains the best performance for regression, and minor improvement achieved after 1-order wavelet decomposition; for price movement, no improvement after changing the number of input features or with the model W-LSTM has been spotted for the same network structure and hyper-parameters, and enlarging time step and batch size will improve accuracy and Matthews correlation coefficient despite of number of input and model used in this paper.
Piyush Kumar Yadav, Rajnish Bhasker, Albert Alexander Stonier, Geno Peter · 6 authors
Abstract Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities.
Bitcoin is sometimes called the new gold, replacing gold as a hedge against inflation, and research on the relationship between bitcoin and gold has important practical implications. This paper first calculates the correlation between bitcoin and gold. By introducing the calculation of dynamic penalty coefficient, the double stock portfolio investment problem is transformed into the single stock purchase investment problem, which greatly reduces the difficulty of feature engineering and model application. In terms of decision model, deep reinforcement learning (PPO algorithm) is used to make quantitative investment decisions, and the expected data in SLTM is taken as the input data of deep reinforcement learning, which is combined with deep reinforcement learning. Compared with machine learning quantitative investment decisions, after a period of learning, the accuracy rate and returns have been substantially improved.
Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.
Sarat Chandra Nayak, Subhranginee Das, Satchidananda Dehuri, Sung‐Bae Cho
Cryptocurrencies have carved out a significant presence in financial transactions during the past few years. Cryptocurrency market performs similarly to other financial markets with considerable nonlinearity and volatility and its prediction is a growing research area. It is challenging to capture the inherent uncertainties connected with cryptocurrency using the currently used conventional methodologies. The popularity of random vector functional link networks (RVFLN) is attributed to its simple structural layout, quick rate of learning, and enhanced generalization ability. It computes the output layer weights using non-iterative techniques like least square methods or iterative techniques like gradient methods, and assigns hidden neuron parameters at random. Random initialization of non-optimal hidden neuron settings, however, degrades the performance. Population-based metaheuristics are a superior option to random initialization for determining the ideal parameters and avoiding the problem of local optima stagnation. In the current article, an elitist artificial electric field algorithm (eAEFA) for training RVFLN is proposed. Here, eAEFA is utilized to create an ideal RVFLN by determining the weights and biases of the hidden layer connections. The elitism method is used by AEFA to maximize its strength. Here, the most suitable entities are directly inserted to create the population of the following generation. By predicting the closing values of six widely used cryptocurrencies, including Bitcoin, Litecoin, Ethereum, ZEC, XLM, and Ripple, one may determine how well the eAEFA+RVFLN model is performing. For comparison study, models including ARIMA, multi-layer perceptron (MLP), basic RVFLN, support vector regression (SVR), LSTM, GA trained RVFLN, and AEFA trained RVFLN are also constructed concurrently. In terms of performance and statistical significance testing, the suggested eAEFA+RVFLN findings outperform the comparator models. On an average, it achieves a MAPE (mean absolute percentage of error) value of 0.0573, R2(coefficient of determination) of 0.9589, POCID (prediction of change in direction) of 0.9676, RMSE (root mean squared error) of 0.0685, MAE (mean absolute error) of 0.0727 and an average rank of 1.346; as a result, it is possible to recommend it as a useful financial forecasting tool.
The purpose of this research is to compare ARIMA and Prophet algorithms and find the best algorithm for forecasting bitcoin prices. The dataset is two years historical bitcoin data between February 2019 and 2021. The data is segmented into daily, weekly, and monthly period category. Both algorithms are built into a univariate model that only receive 2 features for training the model. Several ARIMA models is developed for each dataset interval. After that, the parameter of each model will be cross-referenced to each other to obtain the best parameter combination. Meanwhile, Prophet model will be developed using automatic and manual tuning. Then again parameter value of each model will be cross-referenced to each other to obtain the best parameter combination. Evaluation of the training model is done by calculating the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Rooted Mean Squared Error (RMSE). The results showed that the best model for the daily and weekly data category was the Prophet algorithm, while for the monthly data category was the ARIMA algorithm.
Aleksandar Petrović, Luka Jovanović, Miodrag Živković, Nebojša Bačanin · 6 authors
The interest for cryptocurrencies is high and hence this work focuses on providing a practical real-world application of the swarm metaheuristics and long short term memory model (LSTM).The goal is price forecasting which is interesting due to the high volatility of the cryptocurrencies.The authors apply LSTM for the solution of the problem which has been proven to reap results with this type of problem.The LSTM is further optimized by a swarm metaheuristic -arithmetic optimization algorithm (AOA).The solution was tested alongside familiar high-performing competitors with the use of standard metrics mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).These metrics have been used for comparison between the solutions, upon which the proposed solution obtained overall best performance that testifies to the improvement of the solution.
Joey H. Li, Münür Sacit Herdem, Jatin Nathwani, John Z. Wen
Information technologies involving artificial Intelligence, big data, Internet of Things devices and blockchain have been developed and implemented in many engineering fields worldwide. Existing review articles focus on developments and characteristics of individual topics and the associated deployment in the energy sector. These technologies, all based on communication, information, and data analysis, are naturally coherent and integrable. This article reviews the literature and patents in four closely related fields and aims to provide a holistic view of how they are related and their integrability in relation to smart energy management strategies. Artificial intelligence models forecast energy use and load profiles as well as schedule resources to ensure reliable performance and effective utilization of energy resources. Training artificial intelligence models requires immense volumes of data. Utilizing big data systems and data mining enables the discovery of new functions and relationships, which determines the performance of artificial intelligence. Data mining also refines the information; thus, artificial intelligence is trained iteratively with more accurate data. Smart energy management can be further enhanced through advanced digital technologies like Internet of Things and blockchain. An Internet of Things platform containing edge, fog and cloud layers helps connect artificial intelligence to other hardware and software devices and systems. Furthermore, an Internet of Things platform efficiently transmits and stores data, improving access and availability to stakeholders for data mining. Emerging technologies such as blockchain and cryptocurrency facilitate energy trading and can be designed in the cloud layer of an Internet of Things platform to supplement data storage. Providing an efficient and seamless integration of artificial intelligence, big data, and advanced digital technologies will be an important factor in the emerging transition of the energy sector to a lower-carbon system.
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.
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.
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.