Proof-of-Work (PoW) systems face critical challenges, including excessive energy consumption and the centralization of mining power among entities with expensive hardware. Static mining pools exacerbate these issues by reducing competition and undermining the decentralized nature of blockchain networks, leading to economic inequality and inefficiencies in resource allocation. Their reliance on centralized pool managers further introduces vulnerabilities by creating a system that fails to ensure secure and fair reward distribution. This paper introduces a novel Collaborative Proof-of-Work (CPoW) mining approach designed to enhance efficiency and fairness in the Ethereum network. We propose a dynamic mining pool formation protocol that enables miners to collaborate based on their computational capabilities, ensuring fair and secure reward distribution by incorporating mechanisms to accurately verify and allocate rewards. By addressing the centralization and energy inefficiencies of traditional mining, this research contributes to a more sustainable blockchain ecosystem.
Abstract Plant diseases are considered as the major bottleneck for the farmers to monitor and diagnosis with the super intelligent methods. With the onset of Artificial Intelligence, Internet of Things(IoT), predicting the plant diseases in early stages has given the bright light of hope to farmers for boosting the productivity of agriculture in which increases the country’s economy. But the current advances in the IoT-AI-driven data gathers data from the agricultural fields and integrates the strong communication system for the early prediction and diagnosis process. Though these intelligent drive systems have several advantages, these procedure have been suffering from the varied security challenges which accelerates an outcry for a cognitive systems in the form of data breaches and privacy problems. The protection of patient data remains a significant concern due to the sensitivity and value of healthcare information, especially when transmitted over the Internet. This has heightened the demand for secure systems to safeguard against data breaches and privacy issues. Similarly, in agriculture, security challenges have been addressed using Web 3.0 and Blockchain technologies, which are favored for their immutable and decentralized features. This research proposes an advanced Web 3.0 Ensemble hybrid blockchain framework to enhance authentication security within the agricultural sector. To improve the authentication process further, chaotic maps are used to generate highly dynamic hashes during the creation of genesis blocks, ensuring that all data is securely stored in the recommended approach. The framework was tested on the Ethereum blockchain using Web 3.0, with Python 3.19 as the primary programming language for developing various interfaces. The security strength of the framework was thoroughly assessed using NIST standard tests, and its robustness was compared with other blockchain models. The results demonstrate that the proposed framework provides stronger defenses against various attacks and surpasses varied approaches in terms of complexity and robustness.
Li Yi Thong, Ricky Chee Jiun Chia, Mohd Fahmi Ghazali
Research Question: Does uncertainty indices have impact on cryptocurrency? Motivation: Most of the previous study investigate the impact of geopolitical risk and economic policy uncertainty on Bitcoin only and less research investigate the long run and short run relationship between the uncertainty indices and cryptocurrency. Hence, this study investigates whether the economic policy uncertainty, geopolitical risk and US equity market uncertainty have an impact on Bitcoin, Ethereum and Binance Coin by the multivariate VAR Granger non-causality. Idea: This study applied three different uncertainty indices (geopolitical risk, economic policy uncertainty and US equity market uncertainty) and top three ranking cryptocurrency (Bitcoin, Ethereum and Binance Coin) to investigate and compare the impact of uncertainty indices on cryptocurrency with different uncertainty conditions and applied top three ranking cryptocurrency in cryptocurrency market to reinforce the result. Data: This study applied monthly data with 42 observations which cover the period of December 2017 until May 2021 and data for cryptocurrency extracted from investing.com, while the uncertainty indices from policyuncertainty.com. Method/Tools: This study utilize multivariate VAR Granger non-causality to examine the cointegration relationship between the cryptocurrency and uncertainty indices. Findings: The results show that the economic policy uncertainty, geopolitical risk and US equity market uncertainty cointegrated with Bitcoin, while Binance Coin cointegrated with geopolitical risk only. Hence, the economic policy uncertainty, geopolitical risk and US equity market uncertainty plays a vital role in the Bitcoin prediction and geopolitical risk plays an important role to forecast the Binance Coin. Contributions: The Bitcoin investors may focus on the changes in economic policy uncertainty, geopolitical risk and US equity market uncertainty to predict the Bitcoin return, and Binance Coin investors focus on the geopolitical risk.
This study examines the influence of cryptocurrency's environmental footprint on market behavior through an analysis of 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum. Using a vector autoregression (VAR) model, it explores the relationship between social media discussions on environmental issues, electricity use, and cryptocurrencies' market dynamics. We find a negative correlation between environmental discussions and Bitcoin volatility. Moreover, real electricity use has a more pronounced impact than social media discussions on both Bitcoin and Ethereum volatility. This indicates that crypto market investors prioritize real-world indicators over information from social media discussions. The study also reveals a bidirectional relationship between Bitcoin volatility and environmental posts, highlighting the complex interplay between market behavior and public discourse on environmental matters in the cryptocurrency domain. These results suggest the need for policies that limit energy consumption due to mining, promote renewable energy, and enhance investor education on environmental impacts to support sustainable practices in the cryptocurrency market.
Blockchain technology has emerged as a transformative solution for securing distributed networks, offering decentralized and immutable data management. However, the resilience of blockchain systems faces challenges from various security threats, including double-spending, Sybil attacks, and vulnerabilities in smart contracts. This paper explores the effectiveness of various blockchain security protocols in enhancing the security and stability of distributed networks. The study provides a comprehensive review of cryptographic techniques, consensus algorithms, and privacy-enhancing technologies, such as Zero-Knowledge Proofs and Multi-Party Computation. Through a detailed analysis of case studies involving Bitcoin, Ethereum, and Hyperledger Fabric, the paper highlights the strengths and limitations of different security protocols. Additionally, the paper discusses the future direction of blockchain security, including the impact of emerging threats such as quantum computing on current security measures. The findings emphasize the need for ongoing innovation in security protocols to ensure the long-term resilience of blockchain networks. The paper concludes with recommendations for improving the security frameworks in both public and permissioned blockchains, with a focus on scalability, privacy, and resistance to emerging attacks.
The article investigates the process of block formation in blockchain networks and the impact of node network architecture and consensus algorithms on their scalability and performance. Analysis of blockchain system scalability is important due to problems that arise when network load increases, particularly the increase in the number of block forks and transaction confirmation times. The research focuses on studying the impact of network delays and the choice of consensus algorithm on the performance and scalability of blockchain networks. The main attention is devoted to mathematical models that describe block formation, as well as the analysis of factors affecting transaction processing speed and throughput. The primary consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), are considered, and their impact on scalability in implementations based on the Ethereum Virtual Machine (EVM) and Bitcoin is compared. Experimental studies using Geth and Amazon cloud services revealed that the application of the Proof of Stake (PoS) consensus algorithm increases network performance by reducing the complexity of the block formation process in blockchain networks by 99% and accelerates consensus achievement by 70% compared to Proof of Work (PoW). It was also established that increasing the number of nodes from 5 to 50 reduces the network's throughput by almost 10%, and the average confirmation time doubles. The obtained results are aimed at solving the scalability issue by reducing transaction confirmation times for the implementation of decentralized technologies in the Internet of Things (IoT) sphere, where processing speed and storage of large volumes of data are critically important. Keywords: blockchain, block formation, consensus algorithms, decentralized technologies, Ethereum Virtual Machine (EVM), Internet of Things (IoT), mathematical modeling, network delays, scalability.
Decentralized Finance (DeFi) is revolutionizing the way individuals and institutions engage with financial services by removing intermediaries and offering decentralized alternatives to traditional banking and finance systems. This paper explores the rapidgrowth and impact of DeFi on global financial systems, focusing on key protocols such as Uniswap, Aave, and Compound. Using both qualitative and quantitative methodologies, including case studies and comparative analyses, the research examines the evolution of DeFi in terms of Total Value Locked (TVL), transaction costs, security challenges, and user adoption. The findings reveal that DeFi platforms have experienced exponential growth in liquidity, with TVL across major protocols increasing from $50 million in January 2020 to over $100 billion by January 2024. Uniswap alone saw its TVL grow from $50 million to $15 billion during the same period. DeFi significantly reduces transaction costs, with cross-border fees averaging $7 on Uniswap, compared to $35 in traditional banks. However, Ethereum gas fees remain volatile, exceeding $50 during peak congestion periods. Despite these cost benefits, the study also identifies security as a major concern, with 22 significant security incidents reported in DeFi between2020 and 2023, resulting in substantial financial losses. Additionally, the lack of clear regulatory frameworks continues to pose challenges to broader adoption. This research concludes that while DeFi has the potential to disrupt traditional financial systems, its long-term success depends on addressing these technical and regulatory challenges. The adoption of Layer-2 scaling solutions, along with improvements in security and regulatory clarity, will be essential for ensuring the continued growth and stability of the DeFi ecosystem.
This paper investigates the potential of cryptocurrencies, particularly Bitcoin and Ethereum, as viable hedges against inflation, in comparison to traditional assets like gold. In light of increasing global inflation, fueled by economic crises and expansive monetary policies, investors are seeking dependable strategies to safeguard their purchasing power. Cryptocurrencies have attracted interest due to their finite supply and decentralized characteristics, which could theoretically position them as suitable inflation hedges. Nonetheless, their significant volatility and speculative traits raise concerns about their reliability in fulfilling this role. This research consolidates insights from three prominent studies on the relationship between cryptocurrency and inflation, analyzing their methodologies and outcomes to evaluate the effectiveness of cryptocurrencies as inflation hedges. In summary, although cryptocurrencies may occasionally respond positively to inflation surges, their volatility and speculative nature hinder their ability to serve as consistent inflation hedges. The findings indicate that cryptocurrencies are currently more suited as high-risk speculative investments rather than reliable stores of value against inflation. Keywords :- Cryptocurrencies , Inflation hedge , Bitcoin , Volatility , Speculative investment , Purchasing power , Gold , Decentralization , Asset reliability, Economic crises
"A distributed database that maintains an ever-expanding list of ordered records, called blocks," is how a blockchain is defined.These parts are connected by the use of cryptography. A timestamp contain by each, the preceding block of a cryptographic hash , and with a transaction information. Also we can say that distributed, public, decentralized digital ledger that keeps track of transactions across several computers is called a blockchain. Its goal is to stop record tampering without interfering with network consensus or all subsequent blocks. Because blockchain and smart contracts are developed using non-standard software life cycles, there may be security flaws and difficulties in getting users to adopt the technology. For instance, distributed applications may not receive regular updates or may have bugs that can only be fixed by releasing a new version. A detailed review of smart contracts was covered in this publication. In terms of security, privacy, communication channel, etc., it further differentiated and contrasted the security of smart contracts with that of traditional security. This study also discusses other smart contract systems, including Stellar, Monax, Ethereum, Bitcoin, and Lisk. For smart contracts certain suggested methods are applied in various contexts to address security risks. Furthermore, also smart contract classification of the security application was put out in an effort to address some of the shortcomings. Additionally, the paper offers a thorough security scenario for smart contracts using several methods. Finally, the dangers and weaknesses of the smart contracts that might lead to an attack are listed. Here we can find and focuses on security risks and weaknesses specific to smart contracts.
This research finds the application of Dynamic Time Warping (DTW) with a Long Short-Term Memory (LSTM) to create a hybrid model for predicting Ethereum (ETH) prices. Cryptocurrencies in general are considered as highly volatile assets, ETH being no exception, which presents challenges and opportunities for investors. Machine Learning models have shown promise in time-series and stock price prediction; however, integrating an algorithm like DTW can enhance the accuracy of the model by finding historical sequences that closely represents the current pattern. The study utilizes daily price data of Ethereum from July 2023 to July 2024, focusing on key metrics such as open, close, high, low, and trading volume. The hybrid and LSTM baseline model were tested for 10 randomly chosen seeds and Root Mean Square Error (RMSE) was used to evaluate performance. The hybrid model better predicted the true ETH price by 23.4% as compared to the baseline LSTM model and statistical evidence further confirms the significance of these results. These findings suggest that the hybrid model provides an approach for Ethereum price prediction, offering new insights for people looking to invest in cryptocurrencies.
The rapid evolution of Ethereum’s infrastructure calls for innovative mechanisms to enhance scalability, security, and performance. This paper introduces BeamSNARKS, a cutting-edge framework designed to address critical challenges in zero-knowledge proof systems. BeamSNARKS encompasses two groundbreaking innovations: the Dynamic zkSNARKS Generation Optimization Mechanism and the Dynamic SNARKification Technology. The former revolutionizes computational efficiency by dynamically retrieving state data relevant to proof generation, minimizing bandwidth and storage requirements while maintaining validation accuracy. The latter introduces adaptive circuit design and hierarchical proof aggregation to optimize transaction throughput and reduce the computational and financial overhead of Layer 1 submissions. Together, these innovations establish BeamSNARKS as a pivotal advancement in scalable, efficient, and resource-optimized zero-knowledge proof systems. Through comprehensive analysis and targeted experiments, this paper evaluates the performance of BeamSNARKS’s innovations, demonstrating their potential to transform Ethereum’s decentralized ecosystem and lay the groundwork for future high-throughput applications.
The Industrial Internet of Things (IIoT) refers to a structure where multiple devices and sensors communicate with each other over a network. As the number of internet-connected devices increases, so does the number of attacks on these devices. Therefore, it has become important to secure the data and prevent potential threats to the data in factories or workplaces. In this study, a deep learning-based architecture was used to determine whether the data collected from IIoT sensors was under attack by looking at network traffic. The data that was not exposed to attacks was stored on the Ethereum Blockchain network. The Ethereum blockchain network ensured that sensor data was stored securely without relying on any central authority and prevented data loss in case of any attack. Thanks to the communication process over the blockchain network, updating and sharing data was facilitated. The proposed deep learning-based intrusion detection system separated normal and anomaly data with 100% accuracy. The anomaly data were identified with an average of 95% accuracy for which attack type they belonged to. The data that was not exposed to attacks was processed on the blockchain network, and an alert system was implemented for the detected attack data. This study presents a method that companies can use to secure IIoT sensor data.
Yelizaveta Vitulyova, Inabat Moldakhan, P. E. Grigoriev, Ibragim Suleimenov
It is shown that the statistics of transactions of the Ethereum cryptocurrency obeys well-defined patterns. Log dependency <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>ln</mml:mi><mml:mo></mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math> of the number of users <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:math> who carried out <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> transactions with the use of Ethereum cryptocurrency during a specific month on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:mrow><mml:mi>ln</mml:mi><mml:mo></mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math> is a linear one with high accuracy: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:mrow><mml:mi>ln</mml:mi><mml:mo></mml:mo><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mi>b</mml:mi><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mfenced></mml:mrow><mml:mi>ln</mml:mi><mml:mo></mml:mo><mml:mi>n</mml:mi><mml:mo>+</mml:mo><mml:mi>a</mml:mi><mml:mrow><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math> . Similar statistical patterns are obtained for bitcoin transactions. It has also been established that the behavior of the coefficient <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:mrow><mml:mi>b</mml:mi></mml:mrow></mml:math> appearing in this dependence corresponds with high accuracy to the Bass diffusion model, which describes the dynamics of innovation implementation. It is shown that after the completion of the initial stage of the implementation of the Ethereum and bitcoin cryptocurrencies (since the beginning of 2018), the values of the coefficients a and b are approaching constants. On this basis, a method is proposed for identifying space weather factors on the economic behavior of the human population. In particular, it is shown that the analysis of the cross-correlation between the ratio <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m7"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mi>b</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math> for the Ethereum cryptocurrency and the Ap-index of geomagnetic activity gives an example of additional tools allowing to reveal the influence of space weather factors on the economic behavior of people on a global scale.
Blockchains have sparked global interest in recent years, gaining importance as they increasingly influence technology and finance. This thesis investigates the robustness of blockchain protocols, specifically focusing on Ethereum Proof-of-Stake. We define robustness in terms of two critical properties: Safety, which ensures that the blockchain will not have permanent conflicting blocks, and Liveness, which guarantees the continuous addition of new reliable blocks. Our research addresses the gap between traditional distributed systems approaches, which classify agents as either honest or Byzantine (i.e., malicious or faulty), and game-theoretic models that consider rational agents driven by incentives. We explore how incentives impact the robustness with both approaches. The thesis comprises three distinct analyses. First, we formalize the Ethereum PoS protocol, defining its properties and examining potential vulnerabilities through a distributed systems perspective. We identify that certain attacks can undermine the system's robustness. Second, we analyze the inactivity leak mechanism, a critical feature of Ethereum PoS, highlighting its role in maintaining system liveness during network disruptions but at the cost of safety. Finally, we employ game-theoretic models to study the strategies of rational validators within Ethereum PoS, identifying conditions under which these agents might deviate from the prescribed protocol to maximize their rewards. Our findings contribute to a deeper understanding of the importance of incentive mechanisms for blockchain robustness and provide insights into designing more resilient blockchain protocols.
The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token transfer and diverse transaction logic. However, illegal behaviors, such as financial fraud, hacker attacks, and money laundering, are rampant in the blockchain ecosystem and seriously threaten its integrity and security. In this paper, we propose a novel double graph-based Ethereum account de-anonymization inference method, dubbed DBG4ETH, which aims to capture the behavioral patterns of accounts comprehensively and has more robust analytical and judgment capabilities for current complex and continuously generated transaction behaviors. Specifically, we first construct a global static graph to build complex interactions between the various account nodes for all transaction data. Then, we also construct a local dynamic graph to learn about the gradual evolution of transactions over different periods. Different graphs focus on information from different perspectives, and features of global and local, static and dynamic transaction graphs are available through DBG4ETH. In addition, we propose an adaptive confidence calibration method to predict the results by feeding the calibrated weighted prediction values into the classifier. Experimental results show that DBG4ETH achieves state-of-the-art results in the account identification task, improving the F1-score by at least 3.75% and up to 40.52% compared to processing each graph type individually and outperforming similar account identity inference methods by 5.23 % to 12.91 %.
Client diversity is a cornerstone of blockchain resilience, yet most networks suffer from a dangerously skewed distribution of client implementations. This monoculture exposes the network to very risky scenarios, such as massive financial losses in the event of a majority client failure. In this article, we present a novel framework that combines verifiable execution and economic incentives to provably identify and reward the use of minority clients, thereby promoting a healthier, more robust ecosystem. Our approach leverages state-of-the-art verifiable computation (zkVMs and TEEs) to generate cryptographic proofs of client execution, which are then verified on-chain. We design and implement an end-to-end prototype of verifiable client diversity in the context of Ethereum by modifying the popular Lighthouse client and by deploying our novel diversity-aware reward protocol. Through comprehensive experiments, we quantify the practicality of our approach, from overheads of proof production and verification to the effectiveness of the incentive mechanism. This work demonstrates, for the first time, a practical and economically viable path to encourage and ensure provable client diversity in blockchain networks. Our findings inform the design of future protocols that seek to maximize the resilience of decentralized systems.
Baowei Wang, Fengxiao Guo, Yuting Liu, Bin Li · 5 authors
Abstract Voting plays a vital role in democratic societies. Adopting electronic voting can effectively increase voter participation and significantly reduce the financial burden on the organizers. In recent years, with the prevalence of blockchain technology, numerous blockchain-based electronic voting schemes have emerged. Compared with traditional electronic voting schemes, they have more favorable security features. However, existing schemes generally suffer from inefficient voting procedures, limited functionality, and dependence on specific blockchain platforms, making them challenging to deploy in diverse voting scenarios. This paper proposes an efficient and versatile electronic voting scheme on blockchain that addresses these problems using our proposed smart contract-based aggregated blind signature, zero-knowledge proofs, and threshold encryption scheme. In the paper, the scheme’s various features, including security, are analyzed in detail, and the scheme is deployed and tested on the Hyperledger Fabric and Ethereum blockchain platform. The experiment results demonstrate that the voting scheme satisfies the security requirement, and it has outstanding advantages in performance.
Open access
Internet Traffic Analysis and Secure E-voting
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Alison Winkert dos Santos, Luciano Santos Cardoso, Alessandra Bussador
A tecnologia Blockchain tem evoluído significativamente desde sua concepção, tornando-se um pilar central no desenvolvimento de criptomoedas e sistemas descentralizados. Inicialmente, ela forneceu uma estrutura segura e imutável para transações digitais, o que levou à criação e implementação dos contratos inteligentes, que automatizam acordos e operações sem a necessidade de intermediários. Com o tempo, surgiram novas formas de garantir a segurança e eficiência dessas transações, como a transição do Ethereum, em 2022, do mecanismo Proof of Work (PoW) para Proof of Stake (PoS).
Gabriel Fernández-Blanco, Iván Froiz-Míguez, Paula Fraga‐Lamas, Tiago M. Fernández‐Caramés
This paper describes a lightweight proof-of-concept that tackles the problem of academic certificate forgery with the use of a smart contract deployed in the Ethereum blockchain. The implemented application allows to request, update, download and verify students Academic Records (AR) easily. These ARs are backed up by a decentralized storage system based on InterPlanetary File System (IPFS). The ARs modifications are secured and tamper-proof, since they need to be approved by the network. Furthermore, this proof-of-concept was conceived to support future functionalities such as the verification of Curriculum Vitae (CV) merits, so its architecture could be the basis of many other types of decentralized applications. Not only was the application tested in terms of energy efficiency and performance, but it was also deployed on Single-Board Computers (SBCs) to evaluate its performance in an IoT network.
Open access
Advancements in Semiconductor Devices and Circuit Design
Pedro García-Cereijo, Gabriel Fernández-Blanco, Paula Fraga‐Lamas, Tiago M. Fernández‐Caramés
The deterministic nature of blockchains presents a significant challenge to pseudo-random number generation. Conventional seed-based random number generation methods may not be suitable for deterministic environments as they may be predictable and susceptible to attacks. To address this challenge, this paper proposes the integration of a pseudo-random number generation oracle for the nodes of an Ethereum network. Such an oracle acts as an external provider of pseudo-random numbers, generating random data by using the Fortuna algorithm, which can be used by smart contracts and decentralized applications on the blockchain. However, the integration of an oracle raises additional security and reliability concerns as it relies on a central node that impairs the decentralization of the blockchain and depends on the ability of the oracle to provide unpredictable and non-tampered pseudo-random numbers. The presented implementation can be used in different sectors, such as games of chance, random selection and other scenarios where randomness is essential to guarantee fairness and security. Thus, the integration of a pseudo-random number generation oracle into a Ethereum network can significantly improve the functionality and security of such decentralized applications. In order to show the performance of the proposed system, a comparison is presented that evaluates the security improvements with respect to traditional randomization methods within smart contracts.
Modern database systems are expected to handle dynamic data whose characteristics may evolve over time. Many popular database benchmarks are limited in their ability to evaluate this dynamic aspect of the database systems. Those that use synthetic data generators often fail to capture the complexity and unpredictable nature of real data, while most real-world datasets are static and difficult to create high-volume, realistic updates for. This paper introduces CrypQ, a database benchmark leveraging dynamic, public Ethereum blockchain data. CrypQ offers a high-volume, ever-evolving dataset reflecting the unpredictable nature of a real and active cryptocurrency market. We detail CrypQ's schema, procedures for creating data snapshots and update sequences, and a suite of relevant SQL queries. As an example, we demonstrate CrypQ's utility in evaluating cost-based query optimizers on complex, evolving data distributions with real-world skewness and dependencies.
This paper introduces a novel regression model designed for angular response variables with linear predictors, utilizing a generalized Möbius transformation to define the regression curve. By mapping the real axis to the circle, the model effectively captures the relationship between linear and angular components. A key innovation is the introduction of an area-based loss function, inspired by the geometry of a curved torus, for efficient parameter estimation. The semi-parametric nature of the model eliminates the need for specific distributional assumptions about the angular error, enhancing its versatility. Extensive simulation studies, incorporating von Mises and wrapped Cauchy distributions, highlight the robustness of the framework. The model's practical utility is demonstrated through real-world data analysis of Bitcoin and Ethereum, showcasing its ability to derive meaningful insights from complex data structures.
Jimmy Cheung, Smruthi Rangarajan, Amelia Maddocks, Rohitash Chandra
Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme value forecasting. Although deep learning models have been prominent in multi-step ahead prediction, the development and evaluation of quantile deep learning models have been limited. We present a novel quantile regression deep learning framework for multi-step time series prediction. In this way, we elevate the capabilities of deep learning models by incorporating quantile regression, thus providing a more nuanced understanding of predictive values. We provide an implementation of prominent deep learning models for multi-step ahead time series prediction and evaluate their performance under high volatility and extreme conditions. We include multivariate and univariate modelling, strategies and provide a comparison with conventional deep learning models from the literature. Our models are tested on two cryptocurrencies: Bitcoin and Ethereum, using daily close-price data and selected benchmark time series datasets. The results show that integrating a quantile loss function with deep learning provides additional predictions for selected quantiles without a loss in the prediction accuracy when compared to the literature. Our quantile model has the ability to handle volatility more effectively and provides additional information for decision-making and uncertainty quantification through the use of quantiles when compared to conventional deep learning models.