Annals of Data Science最新文献_第8页

On Poisson Moment Exponential Distribution with Associated Regression and INAR(1) Process 带关联回归和INAR(1)过程的泊松矩指数分布

Annals of Data Science Pub Date : 2023-06-08 DOI: 10.1007/s40745-023-00476-2

R. Maya, Jie Huang, M. R. Irshad, Fukang Zhu

{"title":"On Poisson Moment Exponential Distribution with Associated Regression and INAR(1) Process","authors":"R. Maya, Jie Huang, M. R. Irshad, Fukang Zhu","doi":"10.1007/s40745-023-00476-2","DOIUrl":"10.1007/s40745-023-00476-2","url":null,"abstract":"<div><p>Numerous studies have emphasised the significance of count data modeling and its applications to phenomena that occur in the real world. From this perspective, this article examines the traits and applications of the Poisson-moment exponential (PME) distribution in the contexts of time series analysis and regression analysis for real-world phenomena. The PME distribution is a novel one-parameter discrete distribution that can be used as a powerful alternative for the existing distributions for modeling over-dispersed count datasets. The advantages of the PME distribution, including the simplicity of the probability mass function and the explicit expressions of the functions of all the statistical properties, drove us to develop the inferential aspects and learn more about its practical applications. The unknown parameter is estimated using both maximum likelihood and moment estimation methods. Also, we present a parametric regression model based on the PME distribution for the count datasets. To strengthen the utility of the suggested distribution, we propose a new first-order integer-valued autoregressive (INAR(1)) process with PME innovations based on binomial thinning for modeling integer-valued time series with over-dispersion. Application to four real datasets confirms the empirical significance of the proposed model.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1741 - 1759"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43264212","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}

引用次数: 0

A New Compound Distribution and Its Applications in Over-dispersed Count Data 一种新的复合分布及其在过分散计数数据中的应用

Annals of Data Science Pub Date : 2023-06-07 DOI: 10.1007/s40745-023-00478-0

Peer Bilal Ahmad, Mohammad Kafeel Wani

{"title":"A New Compound Distribution and Its Applications in Over-dispersed Count Data","authors":"Peer Bilal Ahmad, Mohammad Kafeel Wani","doi":"10.1007/s40745-023-00478-0","DOIUrl":"10.1007/s40745-023-00478-0","url":null,"abstract":"<div><p>Every time variance exceeds mean, over-dispersed models are typically employed. This is the reason that over-dispersed models are such an important aspect of statistical modeling. In this work, the parameter of Poisson distribution is assumed to follow a new lifespan distribution called as Chris-Jerry distribution. The resulting compound distribution is an over-dispersed model known as the Poisson-Chris-Jerry distribution. As a result of deriving a general expression for the <i>r th</i> factorial moment, we acquired the moments about origin and the central moments. In addition to this, moment’s related measurements, generating functions, over-dispersion property, reliability characteristics, recurrence relation for probability, and other statistical qualities, have also been described. For the goal of estimating parameter of the suggested model, the maximum likelihood estimation and method of moment estimation have been addressed. The usefulness of maximum likelihood estimates has also been taken into consideration through a simulation study. We employed four real life data sets, examined the goodness-of-fit test, and considered additional standards such as the Akaike’s information criterion and Bayesian information criterion. The outcomes are compared with several potential models.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1799 - 1820"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46822534","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}

引用次数: 0

Utilization of Priori Information in the Estimation of Population Mean for Time-Based Surveys 基于时间的调查中先验信息在人口均值估计中的应用

Annals of Data Science Pub Date : 2023-06-05 DOI: 10.1007/s40745-023-00472-6

Sanjay Kumar, Priyanka Chhaparwal

引用次数: 0

LADDERS: Log Based Anomaly Detection and Diagnosis for Enterprise Systems 梯子:基于日志的企业系统异常检测和诊断

Annals of Data Science Pub Date : 2023-06-04 DOI: 10.1007/s40745-023-00471-7

Sakib A. Mondal, Prashanth Rv, Sagar Rao, Arun Menon

{"title":"LADDERS: Log Based Anomaly Detection and Diagnosis for Enterprise Systems","authors":"Sakib A. Mondal, Prashanth Rv, Sagar Rao, Arun Menon","doi":"10.1007/s40745-023-00471-7","DOIUrl":"10.1007/s40745-023-00471-7","url":null,"abstract":"<div><p>Enterprise software can fail due to not only malfunction of application servers, but also due to performance degradation or non-availability of other servers or middle layers. Consequently, valuable time and resources are wasted in trying to identify the root cause of software failures. To address this, we have developed a framework called LADDERS. In LADDERS, anomalous incidents are detected from log events generated by various systems and KPIs (Key Performance Indicators) through an ensemble of supervised and unsupervised models. Without transaction identifiers, it is not possible to relate various events from different systems. LADDERS implements Recursive Parallel Causal Discovery (RPCD) to establish causal relationships among log events. The framework builds coresets using BICO to manage high volumes of log data during training and inferencing. An anomaly can cause a number of anomalies throughout the systems. LADDERS makes use of RPCD again to discover causal relationships among these anomalous events. Probable root causes are revealed from the causal graph and anomaly rating of events using a k-shortest path algorithm. We evaluated LADDERS using live logs from an enterprise system. The results demonstrate its effectiveness and efficiency for anomaly detection.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1165 - 1183"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46232475","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}

引用次数: 0

Jump-Drop Adjusted Prediction of Cumulative Infected Cases Using the Modified SIS Model 使用修改后的 SIS 模型对累计感染病例进行跳跃式下降调整预测

Annals of Data Science Pub Date : 2023-05-15 DOI: 10.1007/s40745-023-00467-3

Rashi Mohta, Sravya Prathapani, Palash Ghosh

{"title":"Jump-Drop Adjusted Prediction of Cumulative Infected Cases Using the Modified SIS Model","authors":"Rashi Mohta, Sravya Prathapani, Palash Ghosh","doi":"10.1007/s40745-023-00467-3","DOIUrl":"10.1007/s40745-023-00467-3","url":null,"abstract":"<div><p>Accurate prediction of cumulative COVID-19 infected cases is essential for effectively managing the limited healthcare resources in India. Historically, epidemiological models have helped in controlling such epidemics. Models require accurate historical data to predict future outcomes. In our data, there were days exhibiting erratic, apparently anomalous jumps and drops in the number of daily reported COVID-19 infected cases that did not conform with the overall trend. Including those observations in the training data would most likely worsen model predictive accuracy. However, with existing epidemiological models it is not straightforward to determine, for a specific day, whether or not an outcome should be considered anomalous. In this work, we propose an algorithm to automatically identify anomalous ‘jump’ and ‘drop’ days, and then based upon the overall trend, the number of daily infected cases for those days is adjusted and the training data is amended using the adjusted observations. We applied the algorithm in conjunction with a recently proposed, modified Susceptible-Infected-Susceptible (SIS) model to demonstrate that prediction accuracy is improved after adjusting training data counts for apparent erratic anomalous jumps and drops.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"959 - 978"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135086225","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}

引用次数: 0

A Novel Stock Trading Model based on Reinforcement Learning and Technical Analysis 一种基于强化学习和技术分析的股票交易模型

Annals of Data Science Pub Date : 2023-05-11 DOI: 10.1007/s40745-023-00469-1

Zahra Pourahmadi, Dariush Fareed, Hamid Reza Mirzaei

{"title":"A Novel Stock Trading Model based on Reinforcement Learning and Technical Analysis","authors":"Zahra Pourahmadi, Dariush Fareed, Hamid Reza Mirzaei","doi":"10.1007/s40745-023-00469-1","DOIUrl":"10.1007/s40745-023-00469-1","url":null,"abstract":"<div><p>This study investigates the potential of using reinforcement learning (RL) to establish a financial trading system (FTS), taking into account the main constraint imposed by the stock market, e.g., transaction costs. More specifically, this paper shows the inferior performance of the pure reinforcement learning model when it is applied in a multi-dimensional and noisy stock market environment. To solve this problem and to get a practical and reasonable trading strategies process, a modified RL model is proposed based on the actor-critic method where we have amended the actor by incorporating three metrics from technical analysis. The results show significant improvement compared with traditional trading strategies. The reliability of the model is verified by experimental results on financial data (S&P500 index) and a fair evaluation of the proposed method and pure RL and three benchmarks is demonstrated. Statistical analysis proves that a combination of a) technical analysis (role-based strategies) and b) RL (machine learning strategies) and c) restricting the action of the RL policy network with a few realistic conditions results in trading decisions with higher investment return rates.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1653 - 1674"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49174695","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}

引用次数: 0

Platform Resource Scheduling Method Based on Branch-and-Bound and Genetic Algorithm 基于分支定界和遗传算法的平台资源调度方法

Annals of Data Science Pub Date : 2023-05-11 DOI: 10.1007/s40745-023-00470-8

Yanfen Zhang, Jinyao Ma, Haibin Zhang, Bin Yue

引用次数: 0

Bayesian Estimation of Multiple Covariate of Autoregressive (MC-AR) Model 自回归（MC-AR）模型多协变量的贝叶斯估计

Annals of Data Science Pub Date : 2023-05-04 DOI: 10.1007/s40745-023-00468-2

Jitendra Kumar, Ashok Kumar, Varun Agiwal

引用次数: 0

A Bayes Analysis of Random Walk Model Under Different Error Assumptions 不同误差假设下随机漫步模型的贝叶斯分析

Annals of Data Science Pub Date : 2023-04-22 DOI: 10.1007/s40745-023-00465-5

Praveen Kumar Tripathi, Manika Agarwal