{"title":"Non-negative Sparse Matrix Factorization for Soft Clustering of Territory Risk Analysis","authors":"Shengkun Xie, Chong Gan, A. Lawniczak","doi":"10.1007/s40745-024-00570-z","DOIUrl":"https://doi.org/10.1007/s40745-024-00570-z","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Kernel Method for Estimating Matusita Overlapping Coefficient Using Numerical Approximations","authors":"Omar M. Eidous, Enas A. Ananbeh","doi":"10.1007/s40745-024-00563-y","DOIUrl":"https://doi.org/10.1007/s40745-024-00563-y","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Maximum Likelihood Estimation for Generalized Inflated Power Series Distributions","authors":"Robert L. Paige","doi":"10.1007/s40745-024-00560-1","DOIUrl":"https://doi.org/10.1007/s40745-024-00560-1","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Farm-Level Smart Crop Recommendation Framework Using Machine Learning","authors":"Amit Bhola, Prabhat Kumar","doi":"10.1007/s40745-024-00534-3","DOIUrl":"https://doi.org/10.1007/s40745-024-00534-3","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reaction Function for Financial Market Reacting to Events or Information","authors":"Bo Li, Guangle Du","doi":"10.1007/s40745-024-00565-w","DOIUrl":"10.1007/s40745-024-00565-w","url":null,"abstract":"<div><p>Observations indicate that the distributions of stock returns in financial markets usually do not conform to normal distributions, but rather exhibit characteristics of high peaks, fat tails and biases. In this work, we assume that the effects of events or information on prices obey normal distribution, while financial markets often overreact or underreact to events or information, resulting in non normal distributions of stock returns. Based on the above assumptions, we for the first time propose a reaction function for a financial market reacting to events or information, and a model based on it to describe the distribution of real stock returns. Our analysis of the returns of China Securities Index 300 (CSI 300), the Standard & Poor’s 500 Index (SPX or S &P 500) and the Nikkei 225 Index (N225) at different time scales shows that financial markets often underreact to events or information with minor impacts, overreact to events or information with relatively significant impacts, and react slightly stronger to positive events or information than to negative ones. In addition, differences in financial markets and time scales of returns can also affect the shapes of the reaction functions.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transmuted Shifted Lindley Distribution: Characterizations, Classical and Bayesian Estimation with Applications","authors":"A. Chakraborty, S. Rana, S. I. Maiti","doi":"10.1007/s40745-024-00562-z","DOIUrl":"https://doi.org/10.1007/s40745-024-00562-z","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Shamsinejad, T. Banirostam, M. Pedram, A. Rahmani
{"title":"A Review of Anonymization Algorithms and Methods in Big Data","authors":"E. Shamsinejad, T. Banirostam, M. Pedram, A. Rahmani","doi":"10.1007/s40745-024-00557-w","DOIUrl":"https://doi.org/10.1007/s40745-024-00557-w","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Shamsinejad, T. Banirostam, M. Pedram, A. Rahmani
{"title":"Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing","authors":"E. Shamsinejad, T. Banirostam, M. Pedram, A. Rahmani","doi":"10.1007/s40745-024-00556-x","DOIUrl":"https://doi.org/10.1007/s40745-024-00556-x","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analyzing Insurance Data with an Alpha Power Transformed Exponential Poisson Model","authors":"M. Meraou, M. Z. Raqab, Fatmah B. Almathkour","doi":"10.1007/s40745-024-00554-z","DOIUrl":"https://doi.org/10.1007/s40745-024-00554-z","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Tahir, Sufyan Ali, Ayesha Sohail, Ying Zhang, Xiaohua Jin
{"title":"Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects","authors":"Muhammad Tahir, Sufyan Ali, Ayesha Sohail, Ying Zhang, Xiaohua Jin","doi":"10.1007/s40745-024-00551-2","DOIUrl":"10.1007/s40745-024-00551-2","url":null,"abstract":"<div><p>Machine learning algorithms can improve the time series data analysis as compared to the traditional methods such as moving averages or auto-regressive approaches. This advancement has helped to unlock several challenging problems since machine learning not only helps to forecast the overall trend of the data, but it also helps to keep the historical track of changes in factors, influencing this trend. These predictions play a pivotal role in almost all areas of research where the observations are time dependent, such as problems ranging from challenges of finance to public health, environmental and climate change challenges. A key challenge of these domains is the higher number of attributes and predictors since managing and manipulating data from many attributes is itself a significant challenge for future forecasting. Addressing these challenges is possible with Recursive Long Short-Term Memory models. The application of such models is crucial, and their efficacy is further amplified when considering transfer learning. During this research, a detailed and comprehensive description of such models is addressed. Practical application is illustrated through an example, emphasizing that these models, when transferred to complex and large datasets using transfer learning, hold great promise.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-024-00551-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667526","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}