Annals of Data Science最新文献

英文中文

Non-negative Sparse Matrix Factorization for Soft Clustering of Territory Risk Analysis 用于领土风险软聚类分析的非负稀疏矩阵因式分解

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-08-10 DOI: 10.1007/s40745-024-00570-z

Shengkun Xie, Chong Gan, A. Lawniczak

引用次数: 0

Kernel Method for Estimating Matusita Overlapping Coefficient Using Numerical Approximations 使用数值近似法估算马图西塔重叠系数的核方法

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-27 DOI: 10.1007/s40745-024-00563-y

Omar M. Eidous, Enas A. Ananbeh

引用次数: 0

Maximum Likelihood Estimation for Generalized Inflated Power Series Distributions 广义膨胀幂级数分布的最大似然估计

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-23 DOI: 10.1007/s40745-024-00560-1

Robert L. Paige

引用次数: 0

Farm-Level Smart Crop Recommendation Framework Using Machine Learning 利用机器学习的农场级智能作物推荐框架

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-20 DOI: 10.1007/s40745-024-00534-3

Amit Bhola, Prabhat Kumar

引用次数: 0

Reaction Function for Financial Market Reacting to Events or Information 金融市场对事件或信息的反应函数

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-17 DOI: 10.1007/s40745-024-00565-w

Bo Li, Guangle Du

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.

观察表明，金融市场中股票收益的分布通常不符合正态分布，而是表现出峰值高、尾部肥大和偏差等特征。在本文中，我们假设事件或信息对价格的影响服从正态分布，而金融市场往往对事件或信息反应过度或反应不足，从而导致股票收益率的非正态分布。基于上述假设，我们首次提出了金融市场对事件或信息的反应函数，并在此基础上建立了描述实际股票收益率分布的模型。我们对中国证券指数 300（沪深 300）、标准普尔 500 指数（SPX 或 S&P 500）和日经 225 指数（N225）在不同时间尺度上的收益率进行分析后发现，金融市场往往对影响较小的事件或信息反应不足，对影响相对较大的事件或信息反应过度，对正面事件或信息的反应略强于负面事件或信息。此外，金融市场和回报时间尺度的不同也会影响反应函数的形状。

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引用次数: 0

Transmuted Shifted Lindley Distribution: Characterizations, Classical and Bayesian Estimation with Applications 变换的移位林德利分布：特征、经典和贝叶斯估计及其应用

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-16 DOI: 10.1007/s40745-024-00562-z

A. Chakraborty, S. Rana, S. I. Maiti

引用次数: 0

A Review of Anonymization Algorithms and Methods in Big Data 大数据中的匿名算法和方法综述

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-13 DOI: 10.1007/s40745-024-00557-w

E. Shamsinejad, T. Banirostam, M. Pedram, A. Rahmani

引用次数: 0

Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing 使用内存处理来表示大数据流匿名化模型

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-13 DOI: 10.1007/s40745-024-00556-x

E. Shamsinejad, T. Banirostam, M. Pedram, A. Rahmani

引用次数: 0

Analyzing Insurance Data with an Alpha Power Transformed Exponential Poisson Model 用阿尔法幂变换指数泊松模型分析保险数据

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-10 DOI: 10.1007/s40745-024-00554-z

M. Meraou, M. Z. Raqab, Fatmah B. Almathkour

引用次数: 0

Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects 开启在线洞察力：LSTM 探索与迁移学习的前景

Q1 Decision Sciences

Annals of Data Science

Pub Date : 2024-07-08 DOI: 10.1007/s40745-024-00551-2

Muhammad Tahir, Sufyan Ali, Ayesha Sohail, Ying Zhang, Xiaohua Jin

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.

与移动平均法或自动回归法等传统方法相比，机器学习算法可以改进时间序列数据分析。由于机器学习不仅有助于预测数据的整体趋势，还有助于对影响这一趋势的各种因素的变化进行历史跟踪，因此这一进步有助于解决一些具有挑战性的问题。这些预测在几乎所有观测数据依赖于时间的研究领域都发挥着关键作用，例如从金融挑战到公共卫生、环境和气候变化挑战等问题。这些领域面临的一个主要挑战是属性和预测因子的数量较多，因为管理和处理来自众多属性的数据本身就是对未来预测的一个重大挑战。利用递归长短期记忆模型可以应对这些挑战。此类模型的应用至关重要，如果考虑到迁移学习，其功效将进一步放大。本研究对此类模型进行了详细而全面的描述。通过一个实例来说明实际应用，强调这些模型在利用迁移学习转移到复杂的大型数据集时大有可为。

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引用次数: 0

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