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Volume 4
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Analytics, Volume 4, Issue 3 (September 2025) – 6 articles

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13 pages, 874 KB  
Article
Bankruptcy Prediction Using Machine Learning and Data Preprocessing Techniques
by Kamil Samara and Apurva Shinde
Analytics 2025, 4(3), 22; https://doi.org/10.3390/analytics4030022 - 10 Sep 2025
Viewed by 119
Abstract
Bankruptcy prediction is critical for financial risk management. This study demonstrates that machine learning models, particularly Random Forest, can substantially improve prediction accuracy compared to traditional approaches. Using data from 8262 U.S. firms (1999–2018), we evaluate Logistic Regression, SVM, Random Forest, ANN, and [...] Read more.
Bankruptcy prediction is critical for financial risk management. This study demonstrates that machine learning models, particularly Random Forest, can substantially improve prediction accuracy compared to traditional approaches. Using data from 8262 U.S. firms (1999–2018), we evaluate Logistic Regression, SVM, Random Forest, ANN, and RNN in combination with robust data preprocessing steps. Random Forest achieved the highest prediction accuracy (~95%), far surpassing Logistic Regression (~57%). Key preprocessing steps included feature engineering of financial ratios, feature selection, class balancing using SMOTE, and scaling. The findings highlight that ensemble and deep learning models—particularly Random Forest and ANN—offer strong predictive performance, suggesting their suitability for early-warning financial distress systems. Full article
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12 pages, 271 KB  
Article
Accurate Analytical Forms of Heaviside and Ramp Function
by John Constantine Venetis
Analytics 2025, 4(3), 21; https://doi.org/10.3390/analytics4030021 - 26 Aug 2025
Viewed by 744
Abstract
In this paper, explicit exact representations of the Unit Step Function and Ramp Function are obtained. These important functions constitute fundamental concepts of operational calculus together with digital signal processing theory and are also involved in many other areas of applied sciences and [...] Read more.
In this paper, explicit exact representations of the Unit Step Function and Ramp Function are obtained. These important functions constitute fundamental concepts of operational calculus together with digital signal processing theory and are also involved in many other areas of applied sciences and engineering practices. In particular, according to a rigorous process from the viewpoint of Mathematical Analysis, the Unit Step Function and the Ramp Function are equivalently performed as bi-parametric single-valued functions with only one constraint imposed on each parameter. The novelty of this work, when compared with other investigations concerning accurate and/or approximate forms of Unit Step Function and/or Ramp Function, is that the proposed exact formulae are not exhibited in terms of miscellaneous special functions, e.g., Gamma Function, Biexponential Function, or any other special functions, such as Error Function, Complementary Error Function, Hyperbolic Function, or Orthogonal Polynomials. In this framework, one may deduce that these formulae may be much more practical, flexible, and useful in the computational procedures that are inserted into operational calculus and digital signal processing techniques as well as other engineering practices. Full article
18 pages, 844 KB  
Article
LINEX Loss-Based Estimation of Expected Arrival Time of Next Event from HPP and NHPP Processes Past Truncated Time
by M. S. Aminzadeh
Analytics 2025, 4(3), 20; https://doi.org/10.3390/analytics4030020 - 26 Aug 2025
Viewed by 371
Abstract
This article introduces a computational tool for Bayesian estimation of the expected time until the next event occurs in both homogeneous Poisson processes (HPPs) and non-homogeneous Poisson processes (NHPPs), following a truncated time. The estimation utilizes the linear exponential (LINEX) asymmetric loss function [...] Read more.
This article introduces a computational tool for Bayesian estimation of the expected time until the next event occurs in both homogeneous Poisson processes (HPPs) and non-homogeneous Poisson processes (NHPPs), following a truncated time. The estimation utilizes the linear exponential (LINEX) asymmetric loss function and incorporates both gamma and non-informative priors. Furthermore, it presents a minimax-type criterion to ascertain the optimal sample size required to achieve a specified percentage reduction in posterior risk. Simulation studies indicate that estimators employing gamma priors for both HPP and NHPP demonstrate greater accuracy compared to those based on non-informative priors and maximum likelihood estimates (MLE), provided that the proposed data-driven method for selecting hyperparameters is applied. Full article
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29 pages, 2318 KB  
Article
A Bounded Sine Skewed Model for Hydrological Data Analysis
by Tassaddaq Hussain, Mohammad Shakil, Mohammad Ahsanullah and Bhuiyan Mohammad Golam Kibria
Analytics 2025, 4(3), 19; https://doi.org/10.3390/analytics4030019 - 13 Aug 2025
Viewed by 591
Abstract
Hydrological time series frequently exhibit periodic trends with variables such as rainfall, runoff, and evaporation rates often following annual cycles. Seasonal variations further contribute to the complexity of these data sets. A critical aspect of analyzing such phenomena is estimating realistic return intervals, [...] Read more.
Hydrological time series frequently exhibit periodic trends with variables such as rainfall, runoff, and evaporation rates often following annual cycles. Seasonal variations further contribute to the complexity of these data sets. A critical aspect of analyzing such phenomena is estimating realistic return intervals, making the precise determination of these values essential. Given this importance, selecting an appropriate probability distribution is paramount. To address this need, we introduce a flexible probability model specifically designed to capture periodicity in hydrological data. We thoroughly examine its fundamental mathematical and statistical properties, including the asymptotic behavior of the probability density function (PDF) and hazard rate function (HRF), to enhance predictive accuracy. Our analysis reveals that the PDF exhibits polynomial decay as x→∞, ensuring heavy-tailed behavior suitable for extreme events. The HRF demonstrates decreasing or non-monotonic trends, reflecting variable failure risks over time. Additionally, we conduct a simulation study to evaluate the performance of the estimation method. Based on these results, we refine return period estimates, providing more reliable and robust hydrological assessments. This approach ensures that the model not only fits observed data but also captures the underlying dynamics of hydrological extremes. Full article
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26 pages, 1566 KB  
Article
Predictive Framework for Regional Patent Output Using Digital Economic Indicators: A Stacked Machine Learning and Geospatial Ensemble to Address R&D Disparities
by Amelia Zhao and Peng Wang
Analytics 2025, 4(3), 18; https://doi.org/10.3390/analytics4030018 - 8 Jul 2025
Viewed by 558
Abstract
As digital transformation becomes an increasingly central focus of national and regional policy agendas, parallel efforts are intensifying to stimulate innovation as a critical driver of firm competitiveness and high-quality economic growth. However, regional disparities in innovation capacity persist. This study proposes an [...] Read more.
As digital transformation becomes an increasingly central focus of national and regional policy agendas, parallel efforts are intensifying to stimulate innovation as a critical driver of firm competitiveness and high-quality economic growth. However, regional disparities in innovation capacity persist. This study proposes an integrated framework in which regionally tracked digital economy indicators are leveraged to predict firm-level innovation performance, measured through patent activity, across China. Drawing on a comprehensive dataset covering 13 digital economic indicators from 2013 to 2022, this study spans core, broad, and narrow dimensions of digital development. Spatial dependencies among these indicators are assessed using global and local spatial autocorrelation measures, including Moran’s I and Geary’s C, to provide actionable insights for constructing innovation-conducive environments. To model the predictive relationship between digital metrics and innovation output, this study employs a suite of supervised machine learning techniques—Random Forest, Extreme Learning Machine (ELM), Support Vector Machine (SVM), XGBoost, and stacked ensemble approaches. Our findings demonstrate the potential of digital infrastructure metrics to serve as early indicators of regional innovation capacity, offering a data-driven foundation for targeted policymaking, strategic resource allocation, and the design of adaptive digital innovation ecosystems. Full article
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26 pages, 1859 KB  
Article
Domestication of Source Text in Literary Translation Prevails over Foreignization
by Emilio Matricciani
Analytics 2025, 4(3), 17; https://doi.org/10.3390/analytics4030017 - 20 Jun 2025
Viewed by 1048
Abstract
Domestication is a translation theory in which the source text (to be translated) is matched to the foreign reader by erasing its original linguistic and cultural difference. This match aims at making the target text (translated text) more fluent. On the contrary, foreignization [...] Read more.
Domestication is a translation theory in which the source text (to be translated) is matched to the foreign reader by erasing its original linguistic and cultural difference. This match aims at making the target text (translated text) more fluent. On the contrary, foreignization is a translation theory in which the foreign reader is matched to the source text. This paper mathematically explores the degree of domestication/foreignization in current translation practice of texts written in alphabetical languages. A geometrical representation of texts, based on linear combinations of deep–language parameters, allows us (a) to calculate a domestication index which measures how much domestication is applied to the source text and (b) to distinguish language families. An expansion index measures the relative spread around mean values. This paper reports statistics and results on translations of (a) Greek New Testament books in Latin and in 35 modern languages, belonging to diverse language families; and (b) English novels in Western languages. English and French, although attributed to different language families, mathematically almost coincide. The requirement of making the target text more fluent makes domestication, with varying degrees, universally adopted, so that a blind comparison of the same linguistic parameters of a text and its translation hardly indicates that they refer to each other. Full article
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