Article
Computer Science, Theory & Methods
Fang Lv, Wei Wang, Linxuan Han, Di Wang, Yulong Pei, Junheng Huang, Bailing Wang, Mykola Pechenizkiy
Summary: This study proposes a quantitative framework for mining trading patterns of pyramid schemes from financial time series data. The framework includes the LoRSD algorithm for sequence de-noising and the Contrast TPM algorithm for mining patterns. The effectiveness of the framework is demonstrated through extensive experiments on financial data.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Hailin Li
Summary: Dynamic time warping combined with time weight analysis can better reflect the importance of different time points, which is significant for time series data mining.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Dianwu Fang, Lizhen Wang, Jialong Wang, Meijiao Wang
Summary: This study focuses on high influence co-location pattern mining in spatial features, proposing a new concept of proximity and a mining framework to discover meaningful patterns. By utilizing attribute descriptors, attribute weights calculation, and influencing metrics construction, high influencing patterns can be efficiently discovered. Improved algorithms are also proposed to enhance efficiency in pattern mining.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Neurosciences
Francisca F. Fernandes, Jonas L. Olesen, Sune N. Jespersen, Noam Shemesh
Summary: MP-PCA denoising is a popular method for denoising MRI data, but in rodent fMRI, thermal noise from coils can affect activation mapping accuracy and violate MP-PCA assumptions. In this study, we developed a method to denoise vendor data in rodent fMRI and evaluated the effects of MP-PCA denoising on activation spreading. Our results showed that MP-PCA denoising improved SNR and Fourier Spectral Amplitude, but also caused activation spreading and smoother functional maps. The optimal denoising window for improved specificity depends on the data's tSNR and functional CNR.
Article
Chemistry, Analytical
Kenan Li, Huiyu Deng, John Morrison, Rima Habre, Meredith Franklin, Yao-Yi Chiang, Katherine Sward, Frank D. Gilliland, Jose Luis Ambite, Sandrah P. Eckel
Summary: Time series classification often relies on machine learning, but there is growing interest in understanding discriminatory features of time series beyond black box models. Time-series shapelets (TSS) is a promising method for identifying discriminative subsequences, with the novel intelligent method Wavelet-TSS (W-TSS) using wavelet transformation discovery for candidate shapelet identification. Compared to previous TSS algorithms, W-TSS is more computationally efficient, accurate, and able to discover more discriminative shapelets without the need for pre-specification of shapelet length.
Article
Computer Science, Information Systems
Zineb Bousbaa, Javier Sanchez-Medina, Omar Bencharef
Summary: Data stream mining can be used to forecast financial time series exchange rate. Traditional static machine learning models are not suitable for the cyclical patterns in financial historical data. This paper proposes a possible methodology that uses incremental and adaptive strategy to cope with instability. The proposed algorithm utilizes online learning and statistical techniques to detect and respond to pattern shifts in the data trend.
Article
Computer Science, Information Systems
Chixuan Wei, Zhihai Wang, Jidong Yuan, Chuanming Li, Shengbo Chen
Summary: A multi-task learning scheme based on Time-Frequency mining is proposed for semi-supervised time series classification. It captures time-frequency information and learns common features through a multi-task learning framework, improving classification performance and achieving state-of-the-art results.
INFORMATION SCIENCES
(2023)
Article
Business, Finance
Jingjian Si, Xiangyun Gao, Jinsheng Zhou, Xian Xi, Xiaotian Sun, Yiran Zhao
Summary: Time series data play a crucial role in financial research, yet data frequency and completeness significantly affect the research outcomes. This study enhances the compressed sensing method for reconstructing financial data and demonstrates its effectiveness in improving reconstruction accuracy.
FINANCE RESEARCH LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Hyeonmo Kim, Heonho Kim, Sinyoung Kim, Hanju Kim, Myungha Cho, Bay Vo, Jerry Chun-Wei Lin, Unil Yun
Summary: Periodic pattern mining is a topic focused on mining periodic event patterns with sufficient confidence. The resulting patterns are used to predict future events and have found applications in various fields such as predicting oil price fluctuations, traffic congestion, human behavior analysis, and sensor-based data analysis. However, current data structures have limitations in terms of computing performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Mauro Silberberg, Hernan E. Grecco
Summary: As monitoring multiple signals becomes more cost-effective, combining them through a data fusion-aware denoising method can produce a more robust estimation of the underlying process. The method presented here, based on the Haar wavelet transform, offers superior performance by trading off resolution against accuracy and taking advantage of correlations between channels. It outperforms standard wavelet methods in cases involving non-linear transformations or reduction of multichannel signals.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Meserret Karaca, Michelle M. Alvarado, Mostafa Reisi Gahrooei, Azra Bihorac, Panos M. Pardalos
Summary: This paper introduces an effective data transformation technique to convert multivariate time series into multivariate sequences and uses a tree-based method to mine frequent patterns. However, this approach is costly in terms of solution time and memory consumption. The study aims to improve computational efficiency for memory while maintaining reasonable solution time.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Computer Science, Theory & Methods
Ane Blazquez-Garcia, Angel Conde, Usue Mori, Jose A. Lozano
Summary: Recent technological advancements have enabled the collection of large amounts of data over time, leading to the generation of time series. Mining this data for outliers has become an important task for researchers and practitioners. This review aims to provide a structured and comprehensive overview of unsupervised outlier detection techniques in the context of time series, presenting a taxonomy based on key aspects characterizing outlier detection methods.
ACM COMPUTING SURVEYS
(2022)
Article
Mathematics, Applied
Ana Lazcano de Rojas
Summary: The performance of neural networks and statistical models in time series prediction depends on the availability of data. Lack of observations affects the representativeness of patterns and trends. Data augmentation techniques can generate additional observations and improve prediction accuracy. This study analyzes the results of two data augmentation techniques applied to a time series and processed by an ARIMA model and a neural network model, showing significant improvement in predictions when using traditional interpolation techniques.
Article
Computer Science, Artificial Intelligence
Andrea Tonon, Fabio Vandin
Summary: This paper investigates the problem of mining statistically significant paths in time series data generated by an unknown underlying network. The challenge lies in the fact that the underlying network is unknown, making it impossible to directly identify such paths. The researchers propose caSPiTa, an algorithm that considers a generative null model based on meaningful characteristics of the observed dataset to efficiently mine large sets of significant paths while ensuring guarantees on false positives.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Jingyu Li, Xuenan Yang, Tao Qian, Qiwei Xie
Summary: This paper explores the application of adaptive Fourier decomposition (AFD) in deconstructing financial time series. After decomposing the series into mono-components using AFD, we reconstruct them to obtain the trend and detailed components. Compared to the Empirical Mode Decomposition (EMD) and Fourier decomposition method (FDM), AFD's extracted trends are more sensitive to peaks and can better track the tendencies of financial time series with fewer energy differences. Additionally, AFD's detailed components better reflect structural breaks in the original time series.
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS
(2023)
Article
Operations Research & Management Science
Yi-Ting Chen, Edward W. Sun, Yi-Bing Lin
ANNALS OF OPERATIONS RESEARCH
(2019)
Article
Operations Research & Management Science
Edward W. Sun, Timm Kruse, Yi-Ting Chen
ANNALS OF OPERATIONS RESEARCH
(2019)
Article
Economics
Yi-Ting Chen, Wan-Ni Lai, Edward W. Sun
COMPUTATIONAL ECONOMICS
(2019)
Article
Management
Yi-Ting Chen, Edward W. Sun, Yi-Bing Lin
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2020)
Article
Operations Research & Management Science
Wan-Ni Lai, Yi-Ting Chen, Edward W. Sun
Summary: The low volatility effect does not always mean low volatility stocks outperform high volatility stocks; in fact, this effect is mainly driven by high volatility stocks with high specific risks. By decomposing volatility into its individual risk components, it is found that volatility increases monotonically with its specific risk component. Returns obtained from the low volatility effect in stocks are primarily driven by the specific risk component rather than the systematic risk component.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Economics
Yi-Ting Chen, Edward W. Sun, Yi-Bing Lin
COMPUTATIONAL ECONOMICS
(2020)
Article
Engineering, Industrial
Edward M. H. Lin, Edward W. Sun, Min-Teh Yu
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2020)
Article
Engineering, Industrial
Yi-Ting Chen, Edward W. Sun, Ming-Feng Chang, Yi-Bing Lin
Summary: The determination of travel time is crucial for logistics companies to optimize their operations, especially for on-time arrivals in the vehicle routing problem. The Traffic Internet of Things (IoT) utilizes data from various sources to analyze real-time traffic status, increasing logistics efficiency for Logistics 4.0. However, the complexity of big IoT data poses challenges in determining travel time in real-time basis. This research proposes a novel method to forecast travel time based on industrial IoT data, successfully enhancing predictive accuracy after empirical comparison with other computational methods.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2021)
Article
Physics, Multidisciplinary
Igoris Belovas, Leonidas Sakalauskas, Vadimas Starikovicius, Edward W. Sun
Summary: This paper extends the application of mixed-stable models to the analysis of financial data, using the German DAX stock index as a case study for 29 companies. The study proposes the smart-Delta method for calculating the probability density function, with the obtained parameter estimates being used for constructing optimal asset portfolios. The impact of accuracy in computing the probability density function and ML optimization on modeling results and processing time is also examined.
Article
Management
Chang-Chih Chen, Chia-Chien Chang, Edward W. Sun, Min-Teh Yu
Summary: This research presents a dynamic control model for income allocation in the presence of market incompleteness. The study finds that the incompleteness of the life insurance market is due to the non-tradability of health status, and the impact of market incompleteness on life insurance demand is highly influenced by health status characteristics.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Operations Research & Management Science
Wan-Ni Lai, Claire Y. T. Chen, Edward W. Sun
Summary: This study uses quantile regressions to determine the optimal quantiles for extracting firm characteristic based risk factors. By examining 23 developed countries, it is found that the optimal quantiles used to construct common factors may differ between factors, but are similar across the North American, Asia-Pacific, and Europe regions.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Economics
Petra P. Simovic, Claire Y. T. Chen, Edward W. Sun
Summary: Using big data to analyze consumer behavior provides effective decision-making tools for preventing customer attrition in CRM. This research proposes a new predictive analytics based on machine learning, enhancing the classification of logistic regression by adding a penalty term. It addresses overfitting and cost balance in big data analysis.
COMPUTATIONAL ECONOMICS
(2023)
Article
Operations Research & Management Science
Claire Y. T. Chen, Edward W. Sun, Ming-Feng Chang, Yi-Bing Lin
Summary: With the increasing environmental concerns and the utilization of big data, smart transportation is transforming logistics business and operations towards a more sustainable approach. This paper presents a new deep learning approach called bi-directional isometric-gated recurrent unit (BDIGRU) for predictive analysis of travel time and business adoption for route planning. The proposed method directly learns high-level features from big traffic data and reconstructs them using its own attention mechanism, achieving significant improvements in predictive accuracy and efficient route determination under uncertainty.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Economics
Edward W. Sun, Yu-Jen Wang, Min-Teh Yu
COMPUTATIONAL ECONOMICS
(2018)
Article
Economics
Yi-Ting Chen, Edward W. Sun, Min-Teh Yu
COMPUTATIONAL ECONOMICS
(2018)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
