Annals of Data Science最新文献_第7页

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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

引用次数: 0

Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data 零膨胀过分散计数数据的计数回归和机器学习技术:在生态数据中的应用

Annals of Data Science Pub Date : 2023-04-13 DOI: 10.1007/s40745-023-00464-6

Bonelwa Sidumo, Energy Sonono, Isaac Takaidza

引用次数: 0

Inferences Based on Correlated Randomly Censored Gumbel’s Type-I Bivariate Exponential Distribution 基于相关随机截尾GumbelⅠ型双变量指数分布的推断

Annals of Data Science Pub Date : 2023-01-31 DOI: 10.1007/s40745-023-00463-7

Hare Krishna, Rajni Goel

{"title":"Inferences Based on Correlated Randomly Censored Gumbel’s Type-I Bivariate Exponential Distribution","authors":"Hare Krishna, Rajni Goel","doi":"10.1007/s40745-023-00463-7","DOIUrl":"10.1007/s40745-023-00463-7","url":null,"abstract":"<div><p>The formal random censoring plan has been extensively studied earlier in statistical literature by numerous researchers to deal with dropouts or unintentional random removals in life-testing experiments. All of them considered failure time and censoring time to be independent. But there are several situations in which one observes that as the failure time of an item increases, the censoring time decreases. In medical studies or especially in clinical trials, the occurrence of dropouts or unintentional removals is frequently observed in such a way that as the treatment (failure) time increases, the dropout (censoring) time decreases. No work has yet been found that deals with such correlated failure and censoring times. Therefore, in this article, we assume that the failure time is negatively correlated with censoring time, and they follow Gumbel’s type-I bivariate exponential distribution. We compute the maximum likelihood estimates of the model parameters. Using the Monte Carlo Markov chain methods, the Bayesian estimators of the parameters are calculated. The expected experimental time is also evaluated. Finally, for illustrative purposes, a numerical study and a real data set analysis are given.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49343107","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

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh 孟加拉国COVID-19病例的贝叶斯分层空间模型

Annals of Data Science Pub Date : 2023-01-22 DOI: 10.1007/s40745-022-00461-1

Md. Rezaul Karim, Sefat-E-Barket

引用次数: 0