Physica A: Statistical Mechanics and its Applications最新文献_第7页

Opinion dynamics in bounded confidence models with manipulative agents: Moving the Overton window

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-21 DOI: 10.1016/j.physa.2025.130379

A. Bautista

引用次数: 0

Parameter identification of the Black-Scholes model driven by multiplicative fractional Brownian motion

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-21 DOI: 10.1016/j.physa.2025.130371

Wentao Hou , Shaojuan Ma

{"title":"Parameter identification of the Black-Scholes model driven by multiplicative fractional Brownian motion","authors":"Wentao Hou , Shaojuan Ma","doi":"10.1016/j.physa.2025.130371","DOIUrl":"10.1016/j.physa.2025.130371","url":null,"abstract":"<div><div>In this paper, we propose a parameter identification method based on deep learning network, which can jointly identify all parameters of the Black–Scholes (BS) model driven by multiplicative fractional Brownian motion (FBM) in a discrete sample trajectory. Firstly, the Convolutional Neural Network (CNN) is combined with the Bi-directional Gated Recurrent Unit (BiGRU) and the attention mechanism (AM) is integrated to construct the new identifier (CBANN). Then, the multiplicative FBM is constructed as the random effect of the BS model, and all the parameters of the model are identified by the new identifier. Finally, extensive numerical simulations are conducted for both known and unknown Hurst exponents, and two empirical studies are performed using real data. The results suggest that, compared to the PENN identifier and the maximum likelihood (ML) identifier, the proposed identifier can simultaneously identify all parameters in the model more quickly and accurately. Additionally, several advantages of the new identifier are discussed, including its strong generalization performance, flexibility in training set proportion settings, and the incorporation of an attention mechanism layer.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"660 ","pages":"Article 130371"},"PeriodicalIF":2.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing technical indicators with deep learning based price forecasting for cryptocurrency trading

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-21 DOI: 10.1016/j.physa.2025.130359

Mingu Kang, Joongi Hong, Suntae Kim

{"title":"Harnessing technical indicators with deep learning based price forecasting for cryptocurrency trading","authors":"Mingu Kang, Joongi Hong, Suntae Kim","doi":"10.1016/j.physa.2025.130359","DOIUrl":"10.1016/j.physa.2025.130359","url":null,"abstract":"<div><div>The rapid development and significant volatility of the cryptocurrency market make price trend prediction highly challenging. Accurate price predictions are crucial for making informed investment decisions that can lead to higher returns. However, few studies have focused on integrating predictions into actionable trading strategies. This study aims to enhance cryptocurrency trading strategies by integrating deep learning-based price forecasting with technical indicators. Twelve deep learning models were developed and their performance in generating trading signals was compared across various cryptocurrencies and forecast periods. These signals were combined with technical indicators and backtested to identify the optimal strategy, evaluated using the Sharpe ratio. Results show that SegRNN outperformed other models in price forecasting, while a strategy combining TimesNet and Bollinger Bands (BB) achieved the highest trading performance in the ETH market with a returns of 3.19, a maximum drawdown (MDD) of -7.46, and Sharpe ratio of 3.56. Additionally, the integration of technical indicators and AI models demonstrated significant improvements at mid-range intervals, particularly at the 4-hour interval, although no improvement was observed at shorter intervals such as 30 minutes. The study concludes that integrating deep learning with technical indicators can significantly improve the robustness and performance of trading strategies in volatile markets.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"660 ","pages":"Article 130359"},"PeriodicalIF":2.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-informed neural networks in iterative form of nonlinear equations for numerical algorithms and simulations of delay differential equations

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-21 DOI: 10.1016/j.physa.2025.130368

Jilong He , Abd’gafar Tunde Tiamiyu

引用次数: 0

Asymmetric autocorrelation in the crude oil market at multiple scales based on a hybrid approach of variational mode decomposition and quantile autoregression

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-20 DOI: 10.1016/j.physa.2025.130384

Xinpeng Ding , Jiayi He , Yali Zhang , Yi Yin

{"title":"Asymmetric autocorrelation in the crude oil market at multiple scales based on a hybrid approach of variational mode decomposition and quantile autoregression","authors":"Xinpeng Ding , Jiayi He , Yali Zhang , Yi Yin","doi":"10.1016/j.physa.2025.130384","DOIUrl":"10.1016/j.physa.2025.130384","url":null,"abstract":"<div><div>Heterogeneous dependence and memory effects are widely recognized in financial markets, including the crude oil future market. However, few studies have examined the correlation between heterogeneous dependence and memory effects. This association reveals differences in the different memory-trait components, yet the literature is lacking. Our study aims to uncover heterogeneous dependence and memory effects on crude oil future returns and their components at multiple scales and to explain the asymmetry of dependence patterns in the crude oil market through the perspective of irrational investor behavior induced by memory effects. The regressions in this study are based on West Texas Intermediate (WTI) crude oil future prices from 1983 to 2023. We propose a hybrid approach that combines variational mode decomposition (VMD) and quantile autoregression (QAR) to process the return and fluctuation series. Similar to the stock market, we find that the QAR coefficients vary across quantiles. The coefficients are positive for the long-term memory component and negative for the anti-persistent component, indicating the momentum and revert effects. The impacts of extreme lagged returns and negative lagged returns on the distribution of coefficients are evident not only in the return series but also in the two components. Lagged fluctuation and extreme lagged fluctuation accelerate the current fluctuation growth at higher quantiles due to rapid accumulation. Finally, the robustness test confirms that the VMD-QAR method is more resistant to noise and sampling disturbances compared to existing methods. Our study contributes to the analysis of the crude oil market in terms of theoretical and analytical methods in finance.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"660 ","pages":"Article 130384"},"PeriodicalIF":2.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Granular convection in a monolayer induced by asymmetric horizontal oscillations

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-20 DOI: 10.1016/j.physa.2025.130355

A.J. Batista-Leyva , D. Hernández-Delfin , Karol Asencio , R.C. Hidalgo , D. Maza

{"title":"Granular convection in a monolayer induced by asymmetric horizontal oscillations","authors":"A.J. Batista-Leyva , D. Hernández-Delfin , Karol Asencio , R.C. Hidalgo , D. Maza","doi":"10.1016/j.physa.2025.130355","DOIUrl":"10.1016/j.physa.2025.130355","url":null,"abstract":"<div><div>Granular systems subjected to vibrations might exhibit convection due to uneven frictional contacts or unbalanced external perturbations. Gravity’s ubiquitousness in most practical applications makes it the predilected selection to trigger the mentioned imbalance. However, we demonstrate that asymmetric horizontal shaking generates convection in a granular monolayer experimentally and numerically by varying the dimensionless acceleration <span><math><mi>Γ</mi></math></span> without gravity influence. Convection is evidenced through non-uniform particle density and granular temperature distributions. Two distinct convection patterns are manifested depending on <span><math><mi>Γ</mi></math></span>. Two roles are observed for low <span><math><mi>Γ</mi></math></span> values, whereas a four-vortex pattern is displayed when <span><math><mi>Γ</mi></math></span> reaches significant values. Results suggest that wall friction influences the resulting convective pattern but is not responsible for global material circulation. Instead, the asymmetry in the energy injection is reflected in asymmetries of the macroscopic gradients of granular temperature, particle density, and momentum transfer, correlating with the overall circulation of the grains.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"660 ","pages":"Article 130355"},"PeriodicalIF":2.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical modeling of queues at multi-lane signalized intersections with a versatile arrival process

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-20 DOI: 10.1016/j.physa.2025.130405

Qiaoli Yang , Linyan Wei , Zufang Dou , Minhao Xu , Xinyu Kuang

{"title":"Numerical modeling of queues at multi-lane signalized intersections with a versatile arrival process","authors":"Qiaoli Yang , Linyan Wei , Zufang Dou , Minhao Xu , Xinyu Kuang","doi":"10.1016/j.physa.2025.130405","DOIUrl":"10.1016/j.physa.2025.130405","url":null,"abstract":"<div><div>In urban environments, vehicles typically travel in platoons due to periodic releases from upstream traffic signals. Consequently, the inter-arrival times of vehicles within a platoon exhibit significant correlations at downstream signalized intersections. To investigate the impact of these correlated arrival characteristics of headways on the queueing process at signalized intersections, this paper proposes a multi-lane stochastic queueing model with periodic vacations based on a continuous-time Markovian arrival process (MAP). We derive the joint probability distribution of queue length, arrival phase, and signal state. This formulation provides an explicit characterization of the randomness and dynamic behavior of the queueing process at signalized intersections. Additionally, we compute performance metrics such as the mean queue length over time in a signal cycle, providing insights into the dynamic evolution of queues under conditions of correlated vehicle arrivals. Furthermore, by the unique structural properties of the MAP, we numerically assess how correlations in the inter-arrival times influence queueing performance at signalized intersections.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"660 ","pages":"Article 130405"},"PeriodicalIF":2.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Expressway traffic state recognition based on multi-source data fusion and multi-view fusion graph neural network under velocity feature mapping

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-20 DOI: 10.1016/j.physa.2025.130395

Jiandong Zhao , Meng Liu , Jin Shen

{"title":"Expressway traffic state recognition based on multi-source data fusion and multi-view fusion graph neural network under velocity feature mapping","authors":"Jiandong Zhao , Meng Liu , Jin Shen","doi":"10.1016/j.physa.2025.130395","DOIUrl":"10.1016/j.physa.2025.130395","url":null,"abstract":"<div><div>To comprehensively extract the time series features of average vehicle velocity data on expressways and their correlation with traffic states, this paper proposes a Multi-view Fusion Chebyshev Graph Convolution Network (MvFCGCN) model for accurately recognizing expressway traffic congestion states. Firstly, we propose a weighted fusion method of checkpoint data and radar velocity data to obtain the traffic state feature vectors, mapping them into heat maps in the form of chromatograms to create the Traffic State Feature Image dataset based on Checkpoint-Radar Data Fusion (TSFI-CRDF dataset). Secondly, a Traffic State Deep Clustering Network (TSDCN) model based on multi-view fusion convolutional neural network and variational autoencoder is constructed to automatically classify and label the traffic state feature images in the TSFI-CRDF dataset. Subsequently, the traffic state feature image data is further mapped into graph structure data, and the MvFCGCN model is constructed based on the Chebyshev graph convolutional neural network with integrated view fusion weights for traffic state recognition. Finally, experimental validation is carried out on the example of checkpoint plate recognition data and radar velocity data collected from the Beijing-Qinhuangdao section of the Beijing-Harbin Expressway. Comparative analyses with models such as Convolution and Self-Attention Network (CoAtNet) are performed, as well as ablation experiments, alongside effect analyses of the TSFI-CRDF dataset. The experimental results demonstrate that the MvFCGCN model achieves an overall recognition accuracy of 95.25 %, outperforming other comparison models. The proposed interpolation method for fusion of checkpoints and radar data effectively restores the original velocity feature of the traffic state.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"660 ","pages":"Article 130395"},"PeriodicalIF":2.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motif-based mix-order nonnegative matrix factorization for community detection

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-20 DOI: 10.1016/j.physa.2025.130350

Xiaotong Bu , Gaoxia Wang , Ximei Hou

{"title":"Motif-based mix-order nonnegative matrix factorization for community detection","authors":"Xiaotong Bu , Gaoxia Wang , Ximei Hou","doi":"10.1016/j.physa.2025.130350","DOIUrl":"10.1016/j.physa.2025.130350","url":null,"abstract":"<div><div>Community structure is one of the important characteristics of complex networks, so it is of great application value to correctly detect community structure in the study of network structure. Nonnegative matrix factorization (NMF) has been proved to be an ideal model of the community detection. Traditional NMF only focuses on the first-order structure (such as adjacency matrix), but does not consider the higher-order structure (such as motif adjacency matrix). However, only considering one of them cannot well represent the global structural characteristics of complex networks. In this paper, we propose a new Mixed-Order Nonnegative Matrix Factorization (MONMF) framework, which can model both first-order and higher-order structures. Previous nonnegative matrix factorization is mostly used in undirected networks, but we will study based on a variety of motif types in directed networks, use motifs to capture higher-order structures in networks, and introduce linear and nonlinear methods to combine the adjacency matrix representing the first-order structure with the motif adjacency matrix representing the higher-order structure to construct a new feature matrix of NMF. At the same time, we introduce the missing edge matrix that characterizes the edgeless connection structure of the network, and gives the expression of the motif adjacency matrix of the three-node open simple motif and the three-node open anchor motif. The MONMF operation is mainly performed on different real networks for open simple motifs and open anchor motifs. Compared with the comparison methods, MONMF can significantly improve the performance of community detection in complex networks.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"661 ","pages":"Article 130350"},"PeriodicalIF":2.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive analysis of the crypto-assets market through multivariate analysis, clustering, and wavelet decomposition

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-01-18 DOI: 10.1016/j.physa.2024.130330

Emiliano Álvarez, Juan Gabriel Brida, Leonardo Moreno, Andrés Sosa

{"title":"Comprehensive analysis of the crypto-assets market through multivariate analysis, clustering, and wavelet decomposition","authors":"Emiliano Álvarez, Juan Gabriel Brida, Leonardo Moreno, Andrés Sosa","doi":"10.1016/j.physa.2024.130330","DOIUrl":"10.1016/j.physa.2024.130330","url":null,"abstract":"<div><div>This research analyzes the relationship between volatility, traded volume and price in the crypto-assets market. First, the relationship between volatility and traded volume is examined, revealing a positive correlation between the two variables across a large number of crypto-assets. This indicates that increased trading volume coincides with increased volatility in crypto-assets prices, an important attribute in a highly volatile financial market. A wavelet analysis is performed in order to cluster crypto-assets according to their price and/or traded volume. It is found that the two main crypto-assets are in the same cluster when only the price variable is analyzed. However, when adding the traded volume variable to the analysis these two crypto-assets separate. This suggests that Bitcoin and Ethereum have similar behavior in price evolution but when analyzed comprehensively their behavior is heterogeneous. This analysis is carried out using a static approach and the results are contrasted using a dynamic approach by studying the evolution of the clusters over time. The results are important for investors seeking to diversify their trading portfolios with the instantaneous information provided by the market (price and volume). Through understanding the relationship between volatility, traded volume and price, investors can make more informed decisions about where to allocate their capital.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"660 ","pages":"Article 130330"},"PeriodicalIF":2.8,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0