Data Science and Management最新文献_第10页

A new prediction NN framework design for individual stock based on the industry environment 基于行业环境的个股预测神经网络框架设计

Data Science and Management Pub Date : 2022-12-01 DOI: 10.1016/j.dsm.2022.09.001

Qing Zhu , Jianhua Che , Yuze Li , Renxian Zuo

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引用次数: 9

Development of a risk index for cross-border data movement 制定跨境数据流动的风险指数

Data Science and Management Pub Date : 2022-09-01 DOI: 10.1016/j.dsm.2022.05.003

Jin Li , Wanting Dong , Chong Zhang , Zihan Zhuo

{"title":"Development of a risk index for cross-border data movement","authors":"Jin Li , Wanting Dong , Chong Zhang , Zihan Zhuo","doi":"10.1016/j.dsm.2022.05.003","DOIUrl":"10.1016/j.dsm.2022.05.003","url":null,"abstract":"<div><p>Cross-border data transmission in the biomedical area is on the rise, which brings potential risks and management challenges to data security, biosafety, and national security. Focusing on cross-border data security assessment and risk management, many countries have successively issued relevant laws, regulations, and assessment guidelines. This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement. From the perspective of a single organization, the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors. Then, the risk assessment index system of cross-border data flow is constructed. A case study of risk assessment in 358 biomedical organizations is carried out, and the suggestions for data management are offered. This study is condusive to improving security monitoring and the early warning of the cross-border data flow, thereby realizing the safe and orderly global flow of biomedical data.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"5 3","pages":"Pages 97-104"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000224/pdfft?md5=310c5e0c23fd9c0c5dba956bc32eb179&pid=1-s2.0-S2666764922000224-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75192932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Long-term forecasting of hourly retail customer flow on intermittent time series with multiple seasonality 基于多季节性间歇时间序列的小时零售客流长期预测

Data Science and Management Pub Date : 2022-09-01 DOI: 10.1016/j.dsm.2022.07.002

Martim Sousa, Ana Maria Tomé, José Moreira

{"title":"Long-term forecasting of hourly retail customer flow on intermittent time series with multiple seasonality","authors":"Martim Sousa, Ana Maria Tomé, José Moreira","doi":"10.1016/j.dsm.2022.07.002","DOIUrl":"https://doi.org/10.1016/j.dsm.2022.07.002","url":null,"abstract":"<div><p>In this study, we address a demanding time series forecasting problem that deals simultaneously with the following: (1) intermittent time series, (2) multi-step ahead forecasting, (3) time series with multiple seasonal periods, and (4) performance measures for model selection across multiple time series. Current literature deals with these types of problems separately, and no study has dealt with all these characteristics simultaneously. To fill this knowledge gap, we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem. Several adaptions and innovations have been conducted, which are marked as contributions to the literature. Specifically, we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance. To gather strong evidence that our ensemble model works in practice, we undertook a large-scale study across 98 time series, rigorously assessed with unbiased performance measures, where a week seasonal naïve was set as a benchmark. The results demonstrate that the proposed ensemble model achieves eye-catching forecasting accuracy.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"5 3","pages":"Pages 137-148"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000273/pdfft?md5=1dd7e0e03e9230a5986c9c5b06d429a0&pid=1-s2.0-S2666764922000273-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91724878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Pearson correlation and transfer entropy in the Chinese stock market with time delay 具有时滞的中国股市的Pearson相关和传递熵

Data Science and Management Pub Date : 2022-09-01 DOI: 10.1016/j.dsm.2022.08.001

Shaowei Peng, Wenchen Han, Guozhu Jia

引用次数: 9

A hybrid differential evolution algorithm for a stochastic location-inventory-delivery problem with joint replenishment 联合补货随机定位-库存-交货问题的混合差分进化算法

Data Science and Management Pub Date : 2022-09-01 DOI: 10.1016/j.dsm.2022.07.003

Sirui Wang, Lin Wang, Yingying Pi

{"title":"A hybrid differential evolution algorithm for a stochastic location-inventory-delivery problem with joint replenishment","authors":"Sirui Wang, Lin Wang, Yingying Pi","doi":"10.1016/j.dsm.2022.07.003","DOIUrl":"https://doi.org/10.1016/j.dsm.2022.07.003","url":null,"abstract":"<div><p>A practical stochastic location-inventory-delivery problem with multi-item joint replenishment is studied. Unlike the conventional location-inventory model with a continuous-review (<em>r</em>, <em>Q</em>) inventory policy, the periodic-review inventory policy is adopted with multi-item joint replenishment under stochastic demand, and the coordinated delivery cost is considered. The proposed model considers the integrated optimization of strategic, tactical, and operational decisions by simultaneously determining (a) the number and location of distribution centers (DCs) to be opened, (b) the assignment of retailers to DCs, (c) the frequency and cycle interval of replenishment and delivery, and (d) the safety stock level for each item. An intelligent algorithm based on particle swarm optimization (PSO) and adaptive differential evolution (ADE) is proposed to address this complex problem. Numerical experiments verified the effectiveness of the proposed two-stage PSO-ADE algorithm. A sensitivity analysis is presented to reveal interesting insights that can guide managers in making reasonable decisions.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"5 3","pages":"Pages 124-136"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000297/pdfft?md5=84902aa89790bfa1a62890f17b06f72d&pid=1-s2.0-S2666764922000297-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91724879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Novel time slicing approach for customer defection models in e-commerce: a case study 电子商务中顾客流失模型的新时间切片方法:一个案例研究

Data Science and Management Pub Date : 2022-09-01 DOI: 10.1016/j.dsm.2022.07.001

Kyriakos Georgiou , Alexandros Chasapis

{"title":"Novel time slicing approach for customer defection models in e-commerce: a case study","authors":"Kyriakos Georgiou , Alexandros Chasapis","doi":"10.1016/j.dsm.2022.07.001","DOIUrl":"10.1016/j.dsm.2022.07.001","url":null,"abstract":"<div><p>In this study, we examine the problem of predicting customer defection in a noncontractual setting. Motivated by recent work on machine learning using multiple time slices, we develop a novel training and testing framework, the sliding multi-time slicing (SMTS) method. We apply this method to data from the largest marketplace in Greece, namely, Skroutz, considering the standard features that account for the important characteristics of customer activity and custom performance metrics aimed at capturing business-related goals established by the company. The dataset comprises customers over a relatively short period, since April 2018, the number of which has also exhibited a significant increase in recent months. Despite these difficulties and the inherent seasonality of customer defection, our results demonstrate that, with SMTS, developing models that outperform previous approaches and optimize decision-making is possible. We validate the approach to a benchmark dataset from the commerce sector and discuss the practical considerations and requirements of the proposed method.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"5 3","pages":"Pages 149-162"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000285/pdfft?md5=90cc770a3700d52be7c17ade53d2e0ae&pid=1-s2.0-S2666764922000285-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89955151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monitoring machine learning models: a categorization of challenges and methods 监测机器学习模型:挑战和方法的分类

Data Science and Management Pub Date : 2022-09-01 DOI: 10.1016/j.dsm.2022.07.004

Tim Schröder, Michael Schulz

引用次数: 10

Assessing spread risk of COVID-19 in early 2020 2020年初评估COVID-19的传播风险

Data Science and Management Pub Date : 2022-08-26 DOI: 10.1016/j.dsm.2022.08.004

S. Lai, I. Bogoch, N. Ruktanonchai, A. Watts, Xin Lu, Weizhong Yang, Hongjie Yu, K. Khan, A. Tatem

引用次数: 1

A hybrid differential evolution algorithm for a stochastic location-inventory-delivery problem with joint replenishment 联合补货随机定位-库存-交货问题的混合差分进化算法

Data Science and Management Pub Date : 2022-08-01 DOI: 10.1016/j.dsm.2022.07.003

Sirui Wang, Lin Wang, Yingying Pi

引用次数: 11

Forecasting hourly retail customer flow on intermittent time series with multiple seasonality 在具有多季节性的间歇时间序列上预测每小时零售客流

Data Science and Management Pub Date : 2022-07-01 DOI: 10.1016/j.dsm.2022.07.002

Martim Sousa, Ana Maria Tom, José Moreira

引用次数: 5