In the IoT, the payment frequently occurs between service users, device owners, and service platforms as they enjoy the services. In line with the incentive payments, micropayment, a small-amount electronic payment system, has been attracting attention as a payment system for applications. At the same time, the payment on a distributed ledger has been attracting attention as a payment method for micropayment, but there is a problem that the transaction fee for micropayment is high from the viewpoint of the frequency of transactions. In this paper, we propose a method that enables IoT transactions to be conducted on IOTA, one of the distributed ledger technologies with no transaction fees and high scalability, for micropayment transactions that can be made with fine granularity of amounts. The proposed method enables IoT transactions with less processing and delay time than a group of payments. In the proposed method, we compared the search time and memory requirements of four search methods for searching transaction records in IOTA. As a result, we confirm a trade-off between the transaction search time and the amount of required memory.
Neural networks (NNs) are well established and widely used in time series forecasting due to their frequent dominance over other linear and nonlinear models. Thus, this paper does not question their appropriateness in forecasting cryptocurrency prices; rather, it compares the most commonly used NNs, i.e. feedforward neural networks (FFNNs), long short-term memory (LSTM) and convolutional neural networks (CNNs). This paper contributes to the existing literature by defining the appropriate NN structure comparable across different NN architectures, which yields the optimal NN model for Bitcoin return forecasting. Moreover, by incorporating turbulent events such as COVID and war, this paper emerges as a stress test for NNs. Finally, inputs are carefully selected, mostly covering macroeconomic and market variables, as well as different attractiveness measures, the importance of which in cryptocurrency forecasting is tested. The main results indicate that all NNs perform the best in an environment of bullish market, where CNNs stand out as the optimal models for continuous dataset, and LSTMs emerge as optimal in direction forecasting. In the downturn periods, CNNs stand out as the best models. Additionally, Tweets, as an attractiveness measure, enabled the models to attain superior performance.
These days, there is a lot of demand for cryptocurrencies, and investors are essentially investing in them. The fact that there are already over 6,000 cryptocurrencies in use worldwide because of this, investors with regular incomes put money into promising cryptocurrencies that have low market values. Accurate pricing forecasting is necessary to build profitable trading strategies because of the unique characteristics and volatility of cryptocurrencies. For consistent forecasting accuracy in an unknown price range, a variation point detection technique is employed. Due to its bidirectional nature, a Bi-LSTM appropriate for recording long-term dependencies in data that is sequential. Accurate forecasting in the cryptocurrency space depends on identifying these connections, since values are subject to change over time due to a variety of causes. In this work, we employ four deep learning-based models that are LSTM, FB-Prophet, LSTM-GRU and Bidirectional-LSTM(Bi-LSTM) and these four models are compared with Silverkite. Silverkite is the main algorithm of the Python library Graykite by LinkedIn. Using historical bitcoin data from 2012 to 2021, we utilized to analyse the models’ mean absolute error (MAE) and root mean square error (RMSE). The Bi-LSTM model performs better than others, with a mean absolute error (MAE) of 0.633 and a root mean square error (RMSE) of 0.815. The conclusion has significant ramifications for bitcoin investors and industry experts.
Peter T. Yamak, Yujian Li, Ting Zhang, Pius Kwao Gadosey
In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification.
Barış CoÅŸkunuzer, Ignacio Segovia-DomÃnguez, Yuzhou Chen, Yulia R. Gel
Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit time-dependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named \textit{Temporal MultiPersistence} (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time. We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times.
Based on Google Trends, searches related to cryptocurrency have significantly increased in the last couple of years. One crucial aid for cryptocurrency traders or investors is the graphical visualization, which shows the time series data of the cryptocurrency prices. However, problems may occur in data visualization, such as visual noise and information loss, which cause perceptual and cognitive errors in data reading. Therefore, good visualization is needed to avoid decision-making mistakes, particularly in the cryptocurrency trade and investment activities. This study aims to investigate the effect of chart design and time interval on the usability of data visualization. The experiments are conducted in two scenarios, i.e., with and without time pressure. The participants recruited in this study were non-experienced and experienced people classified based on their familiarity with cryptocurrency investment/trading. Objective usability testing is performed by eye tracking, while subjective assessment employs the System Usability Scale (SUS) questionnaire. There are four quantitative dependent variables: response time, number of errors, number of fixations, and time to first fixation. The results show that time interval and time pressure significantly affect usability for both groups of respondents. Although chart design does not substantially affect the dependent variables, a candle chart is generally better than a line chart. By comparing all the combinations of chart design and time intervals, this study concluded that combining candle charts with 1-hour or 4-hour time intervals gives the best results for both respondent groups.
Owen Chaffard, Pablo Mollá, Marc Cavazza, Helmut Prendinger
In the recent advancements in application of deep learning to time series forecasting, focus has shifted from training transformers end-to-end to efficiently leveraging the predictive capabilities of Large Language Models (LLMs). Models that encode the time series data to interact with a frozen LLM backbone have been shown to outperform transformers on all benchmark datasets. However, their efficiency on complex datasets, which do not show clear seasonality or trend, remains an open question. In this work, we seek to evaluate the performance of reprogrammed LLMs on the Bitcoin price chart, a financial time series known for its complexity and high volatility. We propose effective methods to improve the performance of Time-LLM, a State-of-the-art (SOTA) method, on such a time series. First, we propose structural improvements to Time-LLM. Second, we suggest an efficient way to handle the non-stationarity of the dataset. Finally, we propose an efficient method for passing additional financial information to the LLM. Our results demonstrate a 50% improvement on the average percentage loss and a 5% increase on accuracy of our adapted Time-LLM architecture on Bitcoin data when compared to SOTA models, including the original Time-LLM model. This highlights the impact on forecast accuracy of domain-specific decision making in data processing and feature selection.
Mehedi Hasan, Md. Tahmid Rahman, Kazi Ahnaf Alavee, Abu Hasnayen Zillanee · 6 authors
In the fast-paced realm of global financial markets, characterized by rapid trading of both stocks and cryptocurren-cies, it has become essential to grasp the influence of sentiment on market dynamics. With more than 630,000 publicly traded companies worldwide and major stock exchanges like the NYSE handling a substantial portion of global equity transactions, the inherent volatility of the stock market is well-established. Over the past decade, various factors have contributed to the consistent fluctuations in stock prices. One key factor is the influence of investor reviews sourced from diverse news outlets and social media platforms such as Twitter. Understanding how these reviews can be collected and effectively summarized is crucial. This paper centers on the intricate field of market sentiment analysis and its profound impact on user sentiment, subsequently affecting price fluctuations in both stocks and cryptocurrencies. In this study, we present a comprehensive exploration of the development and evaluation of an automated sentiment analysis system tailored for summarizing web-based news related to stocks and cryptocurrencies.We have implemented BERT (Bidirectional Encoder Representations from Transformers) in combination with NLTK for text summarization, a highly accurate model with a performance level of 95.84%, as part of our proposed approach.
Temporal Heterogeneous Networks (THNs) are evolving networks that characterize many real-world applications such as citation and events networks, recommender systems, and knowledge graphs. Forecasting THNs involves predicting future connections within a network that evolves over time and comprises diverse types of nodes and interactions with varying temporal dynamics. Although some Graph Neural Networks (GNNs) models have been successfully applied to forecast THNs, there is a lack of a general overview of how the message-passing computation could be extended to treat THNs. Moreover, most of the current solutions exhibit pitfalls in their training and evaluation strategies. Hence, in this work, we propose a graph deep learning framework for THN forecasting. Our framework decomposes the computation of a GNN layer into multiple components and introduces two different schemes to update embedding representations for THNs. This design allows the classification of existing solutions into special instances of our framework and highlights their potential limitations. We also extend the set of benchmarks for THNs by introducing two novel high-resolution temporal heterogeneous graph datasets derived from an emerging Web3 platform and a well-established e-commerce website. Overall, we conducted the first massive evaluation of THNs solutions over four temporal heterogeneous network datasets on two different future link prediction tasks using a fair newly introduced evaluation setting that considers the evolving nature of the data. Based on the limitations of existing solutions, we develop a new model that combines working techniques from previous models and leverages a new embedding update scheme. Experiments show the prediction power of our model compared to current solutions for link prediction in temporal graphs. Moreover, the experimental evaluation highlights the strengths and weaknesses of the different solutions and shows the effectiveness of our framework design.
This paper proposes a unified framework for the detection of statistically significant changes in time series related to Bitcoin transactions. The time locations of these changes are linked to the occurrences of events which could be further investigated aiming to reveal potential illicit activity. The proposed framework includes: (a) the extraction of 28 features of interest in the form of time series from the Bitcoin transaction history; (b) the selection of features among the extracted ones based on the Partition Around Medoids clustering approach; and (c) the change point analysis of the multivariate time series which is formulated by the medoid time series of each cluster. This analysis enables the identification of structural breaks in the underlying behavior of the time series of interest at certain time points. The proposed framework is applied on the Bitcoin transactions of two entities that have been involved in illicit activities, namely Pirate@40, who orchestrated a high-yield investment programme, and the MintPal Bitcoin exchange platform that was hacked. The analysis results indicate that the estimated change points can be linked to certain event occurrences which may affect the transaction activity and could be further investigated for potential links to illicit actions.
Financial markets are complex, evolving dynamic systems. Due to their irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. The objective of this paper is to present this versatile framework and attempt to use it to predict the stock return series of four public-listed companies on the New York Stock Exchange. Our findings coincide with those of Burton Malkiel in his book, A Random Walk Down Wall Street; no conclusive evidence is found that our proposed models can predict the stock return series better than that of a random walk.
In recent years, the attention drawn by cryptocurrencies has increased as their popularity grows rapidly. This situation attracts investors, entrepreneurs, regulators, and the general public. However, these coins may die and become dead coins. A coin is declared dead if no activity is recorded for more than one year. Numerous coins die without completing their one-year timeframe and this issue causes investors to lose a significant amount of money. In this study, we develop a deep neural network architecture based on long short-term memory (LSTM) to predict the death risk of a coin in a specified timeframe. In order to do this, time-series data consisting of the closing price and volume values of 4733 dead coins are utilized. The goal of our model is to inform investors about the death risk of the coin and improve their overall portfolio performance.
Neil Archein I. Gomez, Gernel S. Lumacad, Isabela Loren R. Saludes, Princess Aravela A. Castino · 5 authors
MIR4, is a play to earn game that uses Non-Fungible Tokens (NFT) and cryptocurrency- or in MIR4, Draco Tokens- as a reward. Draco is obtained through mining an in-game resource called Darksteel and is then traded to Wemix Wallet, where real-world money is obtained. Cryptocurrencies are volatile, which gives MIR4 players and traders a decision dilemma of when is the preferable time to buy, sell, or trade Draco Tokens. In this study we present deep learning models, specifically the Long-Short Term Memory (LSTM) neural network, and Neural Prophet (NP) time series machine learning models to forecast future Draco-token exchange value. Historical data of Draco-token value from Yahoo Finance is utilized as a univariate parameter for the analysis, model development, and the forecasting of the future Draco-token exchange values. Performance of formulated models are assessed and compared based on the following regression metrics: RMSE, MSE, MAE and MAPE. Experimental results indicated that the LSTM Neural Network yielded better forecast estimates with lower error than the Neural Prophet. Findings of the study showed that LSTM can be utilized as a tool for forecasting future Draco token exchange values. future research direction suggests improving prediction accuracy by incorporating other parameters such as MIR-4 players sentiments, newly added players, and google search interest over time.
With the construction and development of smart cities, accurate and real-time traffic prediction plays a vital role in urban traffic. However, traffic data has the characteristics of nonlinearity, nonstationary, and complex structure, so traffic prediction has always been a challenging problem. The traditional statistical model is good at dealing with linear data and poor at dealing with nonlinear data. Although the ability to capture nonlinear data has improved, the deep learning approach has difficulty in meeting the real-time requirements of traffic prediction. To solve the above challenges, we propose a novel approach based on the autoregressive integrated moving average model (ARIMA) model and combining empirical mode decomposition (EMD) and singular value decomposition (SVD) technology, i.e., ESARIMA. This method first uses EMD to stabilize the traffic data, then uses SVD to compress data and reduce the noise, so as to improve the efficiency and accuracy of ARIMA model in predicting traffic flow. Finally, we use real data sets to verify the feasibility of ESARIMA. The experimental results show that our method outperforms state-of-the-art baselines.
In this study, a novel approach for feature selection has been presented in order to overcome the challenge of classifying positive and negative risk prediction in the cryptocurrency market, which contains high fluctuation. This approach is based on maximizing information gain with simultaneously minimizing the similarity of selected features to achieve a proper feature set for improving classification accuracy. The proposed method was compared with other feature selection techniques, such as sequential and bidirectional feature selection, univariate feature selection, and least absolute shrinkage and selection operator. To evaluate the feature selection techniques, several classifiers were employed: XGBoost, k-nearest neighbor, support vector machine, random forest, logistic regression, long short-term memory, and deep neural networks. The features were elicited from the time series of Bitcoin, Binance, and Ethereum cryptocurrencies. The results of applying the selected features to different classifiers indicated that XGBoost and random forest provided better results on the time series datasets. Furthermore, the proposed feature selection method achieved the best results on two (out of three) cryptocurrencies. The accuracy in the best state varied between 55% to 68% for different time series. It is worth mentioning that preprocessed features were used in this research, meaning that raw data (candle data) were used to derive efficient features that can explain the problem and help the classifiers in predicting the labels.
The goal of our work was to select a neural network architecture that would give the best prediction of the Bitcoin exchange rate using historical data. Our work fits into the very important topic of predicting the value of the cryptocurrency exchange rate, and makes use of recent data which, as a result of the high Bitcoin exchange rate dynamics of the last year, differs significantly from those of previous years. We propose and test a number of neural network-based architectures and conduct a discussion of the results. Unlike previous state-of-the-art works, we conducted a comprehensive comparison of three different neural network-based models: MLP (multilayer perceptron), LSTM (long short-term memory) and CNN (convolutional neural network). We tested them for a wide range of parameters. The results we present are, to the best of our knowledge, the most up to date when it comes to the application of artificial intelligence methods for the prediction of cryptocurrency exchange rates. The best-performing architectures were used for a website that gives real-time predictions of the Bitcoin exchange rate. The website is available at http://stpbtc-ii.up.krakow.pl/. Source codes of our research are available to download in order to make our experiment reproducible.
Tomáš Šťastný, Jiřà Koudelka, Diana BÃlková, LuboÅ¡ Marek
Cryptocurrencies are a new field of investment opportunities that has experienced a significant growth in the last decade. The crypto market was capitalized at more than USD 3000 bn, having grown from USD 10 m over the period 2011–2021. Generating high returns, investments in cryptocurrencies have also shown high levels of price volatility. By comparing the performance of cryptocurrencies (measured by the crypto index) and standard equities (included in the S&P 500 index), we found that the former has outperformed the latter 14 times over the last two years. In the present paper, we analyzed the 2012–2022 global crypto market developments and main constituents. With a focus on the top 30 cryptocurrencies and their prices, as of 9 April 2022, covering data of the two major market stress events—outbreaks of the COVID-19 pandemic (February 2020) and the Russian invasion of Ukraine (February 2022). We applied the dynamic time warping method including barycentre averaging and k-Shape clustering of time series. The use of the dynamic time warping has been essential for the preparation of data for subsequent clustering and forecasting. In addition, we compared performance of cryptocurrencies and equities. Cryptocurrency time series are rather short, sometimes involving high levels of volatility and including multiple data gaps, whereas equity time series are much longer and well-established. Identifying similarities between them allows analysts to predict crypto prices by considering the evolution of similar equity instruments and their responses to historical events and stress periods. Moreover, we tested various forecasting methods on the 30 cryptocurrencies to compare traditional econometric methods with machine learning approaches.
Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond initial expectations as daily trades exceed $10 billion. As industries become automated, the need for an automated fraud detector becomes very apparent. Detecting anomalies in real time prevents potential accidents and economic losses. Anomaly detection in multivariate time series data poses a particular challenge because it requires simultaneous consideration of temporal dependencies and relationships between variables. Identifying an anomaly in real time is not an easy task specifically because of the exact anomalistic behavior they observe. Some points may present pointwise global or local anomalistic behavior, while others may be anomalistic due to their frequency or seasonal behavior or due to a change in the trend. In this paper we suggested working on real time series of trades of Ethereum from specific accounts and surveyed a large variety of different algorithms traditional and new. We categorized them according to the strategy and the anomalistic behavior which they search and showed that when bundling them together to different groups, they can prove to be a good real-time detector with an alarm time of no longer than a few seconds and with very high confidence.
With time-series data being prevalent everywhere, there is a need to predict this data accurately. This kind of data includes weather data, financial data such as stock price, and cryptocurrency price. Most of the trades in the stock market in this day and age are being made using artificial intelligence. An estimated 50% of trades were done using an algorithm, which increased to 60% in 2020 [1]. This highlights the demand for reliable and accurate predictions. The prediction of the price is very challenging. Some success has been seen when predicting stock prices, but not many studies have been done on cryptocurrency. Cryptocurrency, specifically Bitcoin, has seen a substantial increase in popularity, and the price has reflected this popularity. The price also follows patterns specifically when reaching new all-time highs. In this work, an Artificial intelligence is created and trained on the previous data to observe these patterns and predict the next price. The artificial intelligence chosen for this subject is Long short-term memory (LSTM). LSTMs are capable of finding patterns in time series data. LSTM solves the vanishing gradient problem present in the RNN (Recurrent Neural Network). The Market Price of Bitcoin is used as input here. The data values for input range from 20,000 up to 65,000 in testing. Once an optimal starting point is found, there is an 80/20 split of data, 80 percent of the data is used for training and 20 is used for testing. With the data being split, one of the most important jobs is figuring out the optimal lags (how far back into the past) when used to predict values. This range for this experiment is set to ten previous price days. Epochs (number of iterations) and Batch size (how much of the training data is used per epoch) are tested at different values to find optimal solutions. With batch size values such that batchSize ∈ {20, 21…26} and epochs such that epochs ∈ {10, 20….70}. Overfitting is hard to detect and thus can be an issue with too many epochs and smaller batch sizes (smaller means more of the training data is used). Too little and the LSTM will not learn the data patterns and thus will not have good accuracy. This is why different configurations are used in the experiment to maximize accuracy. This LSTM was used to achieve a Mean Absolute Percentage Error score of 3.23% and a Root Mean Squared Error score of 1892.87 when predicting next-day prices throughout 350.
Stock market price and cryptocurrency price prediction is a very challenging task. We are proposing dynamic algorithms which make use of LSTM and another time Series algorithm, i.e., prophet and we have various trained models on these two algorithms. We will make use of this dynamic algorithm which will self-evaluate different datasets and different pretrained models and will provide us with the best possible output for different test cases. For the longer duration, we are just focusing on up and down, but for the small duration, we are focusing on price-related accuracy. The main and challenging work is to deal with the dynamic dataset, so we require some dynamic algorithm for this.