Article
Computer Science, Artificial Intelligence
Qinghua Gu, Yinxin Chang, Naixue Xiong, Lu Chen
Summary: This paper proposes a hybrid approach based on GBDT, correlation analysis, and EWT to predict the settlement price of LME Nickel. The combination of correlation analysis, wavelet decomposition, and gradient boosting tree helps avoid redundant components and achieves good results on multiple datasets.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Yu Lin, Kechi Chen, Xi Zhang, Bin Tan, Qin Lu
Summary: This study introduces a novel hybrid model for forecasting crude oil futures price using deep learning and wavelet transform methods. Experimental results demonstrate that the model performs well under different training set lengths, and the loss functions are statistically significant.
APPLIED SOFT COMPUTING
(2022)
Article
Environmental Studies
Lijuan Peng, Chao Liang
Summary: This paper investigates the impact of inflation on the crude oil market before and during the COVID-19 outbreak. The findings show that inflation has a significant positive effect on crude oil, especially during the pandemic. The study also reveals that extreme inflation shocks have a greater impact on crude oil markets. Furthermore, inflation provides useful information for forecasting crude oil volatility.
Article
Business, Finance
Kris Ivanovski, Abebe Hailemariam
Summary: This paper models and forecasts the volatility and correlation between oil prices and stock returns using a GAS model based on the score function. The research finds a time-varying relationship between the two variables, with dynamic correlations rising significantly during turbulent events. The results provide useful information for investors, portfolio managers, and market participants.
JOURNAL OF COMMODITY MARKETS
(2021)
Article
Green & Sustainable Science & Technology
Zhaohong Li, Jianfeng Hou, Jianfeng Zhang
Summary: This paper examines the risk spillover effect of the Shanghai crude oil futures market on the exchange rate market and the international crude oil market. The research findings indicate that the Shanghai crude oil futures market has an inverse volatility spillover effect on the onshore exchange rate market and a two-way positive volatility spillover effect on the offshore exchange rate market. Additionally, there is a significant two-way risk spillover between the offshore exchange rate market and the international crude oil market, and a downward risk spillover between the international crude oil market and the onshore exchange rate market.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Economics
Mengxi He, Yaojie Zhang, Danyan Wen, Yudong Wang
Summary: In this study, the s-PCA approach is utilized to predict oil prices, showing superior performance and market-timing advantages. The s-PCA model can identify technical indicators with strong predictive power, offering oil futures investors a larger Sharpe ratio.
Article
Economics
Yun Bai, Xixi Li, Hao Yu, Suling Jia
Summary: The paper introduces two novel indicators of topic and sentiment for short and sparse text data to improve crude oil forecasting performance. Empirical experiments show that AdaBoost.RT with the proposed text indicators outperforms other benchmarks in forecasting performance.
INTERNATIONAL JOURNAL OF FORECASTING
(2022)
Article
Economics
Syed Ali Raza, Amna Masood, Ramzi Benkraiem, Christian Urom
Summary: Economic policy plays a key role in investment and financial decisions. Moreover, the prices of precious metals are greatly influenced by global economic policy uncertainty. This study aims to assess the impact of global economic policy uncertainty on the volatility of gold, palladium, platinum, and silver prices before and during the COVID-19 pandemic. Using the GARCH-MIDAS approach, the results show a significant correlation between global economic policy uncertainty and precious metals price volatility.
Article
Mathematics
Tong Liu, Yanlin Shi
Summary: The recent price crash of the NYMEX crude oil futures contract has resulted in historical movements of relative prices, indicating potential non-stationarity and asymmetric influence in conditional volatility. To address these challenges, a new TZD-GARCH model is proposed and empirically validated with data from INE, BRE, and WTI. The model demonstrates its usefulness in accurately forecasting volatility and has potential applications in hedging risks and identifying market inefficiencies.
Article
Economics
Faramarz Saghi, Mustafa Jahangoshai Rezaee
Summary: Hybrid methods involving wavelet decomposition and fuzzy transform are proposed for predicting OPEC crude oil prices, with L5-ANFIS, L5-MLP, L5-GMDH, and L5-SVR found to be more suitable for the prediction based on comparative results.
COMPUTATIONAL ECONOMICS
(2021)
Article
Mathematics
Nagaraj Naik, Biju R. Mohan
Summary: Volatility in stock prices is influenced by various factors such as demand, supply, economic policy, and company earnings. This study utilized MSGARCH and SETAR models to estimate stock price volatility, with the MSGARCH model performing better.
Article
Economics
Lili Guo, Xinya Huang, Yanjiao Li, Houjian Li
Summary: This paper introduces artificial intelligence methods to evaluate the optimal forecasting strategy for China's crude oil futures price. Using machine learning and considering historical information, volatility, and non-linear features, the study examines the forecasting effects of various models. Results show that the GRU model outperforms other models in terms of forecast accuracy and performance. Additionally, considering multiple influencing factors improves the forecasting accuracy of the proposed models.
Article
Thermodynamics
Manuel Monge, Luis Alberiko Gil-Alana
Summary: This paper analyzes the spatial divergence of crude oil production in the United States, with a focus on PADD 2 and PADD 3. Using fractional integration, fractional cointegration VAR, and wavelet analysis, the study finds that areas such as Anadarko, Appalachia, Haynesville, and Niobrara are most affected by the increase in shale oil production leading to a decrease in WTI crude oil prices.
Article
Automation & Control Systems
Jiaxin Yuan, Jianping Li, Jun Hao
Summary: In this study, a dynamic ensemble forecasting method using clustering approaches is proposed for nonstationary oil prices. Clustering is employed to classify historical observations into clusters, providing a targeted evaluation of individual forecasting models. The proposed model includes a clustering-based weight assignment strategy to balance competitiveness and robustness. Results show that the proposed model outperforms benchmarks and state-of-the-art methods, indicating its competitiveness and robustness. The effectiveness of the proposed model is validated through parameter variation and data missing scenarios, highlighting its potential in improving oil price prediction performance.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Economics
Yue-Jun Zhang, Han Zhang
Summary: This paper examines the smooth and sharp structural changes in crude oil price volatility and evaluates the performance of GARCH models based on these changes for forecasting. The empirical results show that the flexible Fourier form (FFF) GARCH models, which consider smooth shift, accurately capture structural changes and outperform traditional GARCH models in fitting and forecasting. The Markov regime switching (MRS) GARCH model, incorporating regime switching, performs better in fitting but not necessarily in forecasting compared to single-regime GARCH models. Overall, FFF-GARCH models outperform MRS-GARCH models in forecasting crude oil price volatility and portfolio performance.
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)