Traditional Statistical Models Research Articles

Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer

Research questionCan machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?DesignA dual-center retrospective analysis of 1,664 SVBT cycles, including 308 early miscarriage cases, was conducted across two reproductive centers. Multiple machine learning models, such as Logistic Regression, Random Forest, Gradient Boosting, and Voting Classifier, were developed. Metrics including Area Under the Curve(AUC), accuracy, precision, recall, F1 score, and specificity were used to evaluate model performance. Key predictors were identified through Mutual Information and Recursive Feature Elimination (RFE).ResultsMaternal age, paternal age, endometrial thickness, blastocyst quality, and ovarian stimulation parameters were identified as critical predictors. Compared to traditional statistical models such as logistic regression (AUC = 0.584), ensemble models demonstrated significantly improved predictive performance. The Voting Classifier achieved the highest AUC (0.836), accuracy (0.780), precision (0.914), and specificity (0.942), outperforming individual machine learning classifiers. The Gradient Boosting Classifier also exhibited strong performance (AUC 0.831, accuracy 0.777), confirming the effectiveness of ensemble learning in capturing complex predictors of early miscarriage risk.ConclusionEnsemble machine learning models, particularly the Voting Classifier and Gradient Boosting Classifier, significantly improve the prediction of early miscarriage following SVBT. These models provide accurate, individualized risk assessments, enhancing clinical decision-making and advancing personalized care in ART.

Development of a Machine Learning-Based Prediction Model for Sugarcane Yield: A Study with Special Reference to Meerut, Uttar Pradesh, India

Sugarcane is a vital cash crop in India, with Uttar Pradesh being one of the leading producing states. Accurate yield prediction is essential for optimizing agricultural planning, resource allocation, and market strategies. Traditional statistical models often fail to capture the complex, non-linear interactions between climatic and soil parameters affecting yield. This study leverages machine learning (ML) techniques to develop a robust prediction model for sugarcane yield in Meerut, Uttar Pradesh, using historical climatic and soil data from 2013 to 2023. Four ML models—Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Networks (ANN)—were implemented and compared based on Mean Squared Error (MSE) and R² score. The results indicate that ANN achieved the highest prediction accuracy (R² = 0.94), followed by SVR and RFR, while MLR performed comparatively lower. Sensitivity analysis revealed that solar radiation and soil moisture significantly impact yield outcomes. This study highlights the potential of AI-driven agricultural forecasting in precision farming, aiding farmers and policymakers in improving yield management. The findings emphasize the need for integrating ML models into agricultural practices for sustainable crop production and resource efficiency.

Factor Augmented Tensor-on-Tensor Neural Networks

This paper studies the prediction task of tensor-on-tensor regression in which both covariates and responses are multi-dimensional arrays (a.k.a., tensors) across time with arbitrary tensor order and data dimension. Existing methods either focused on linear models without accounting for possibly nonlinear relationships between covariates and responses, or directly employed black-box deep learning algorithms that failed to utilize the inherent tensor structure. In this work, we propose a Factor Augmented Tensor-on-Tensor Neural Network (FATTNN) that integrates tensor factor models into deep neural networks. We begin with summarizing and extracting useful predictive information (represented by the ``factor tensor'') from the complex structured tensor covariates, and then proceed with the prediction task using the estimated factor tensor as input of a temporal convolutional neural network. The proposed methods effectively handle nonlinearity between complex data structures, and improve over traditional statistical models and conventional deep learning approaches in both prediction accuracy and computational cost. By leveraging tensor factor models, our proposed methods exploit the underlying latent factor structure to enhance the prediction, and in the meantime, drastically reduce the data dimensionality that speeds up the computation. The empirical performances of our proposed methods are demonstrated via simulation studies and real-world applications to three public datasets. Numerical results show that our proposed algorithms achieve substantial increases in prediction accuracy and significant reductions in computational time compared to benchmark methods.

Fuel Consumption Prediction for Full Flight Phases Toward Sustainable Aviation: A DMPSO-LSTM Model Using Quick Access Recorder (QAR) Data

Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting in significant nonlinear relationships between segment-specific variables and fuel usage. Traditional statistical and econometric models struggle to capture these relationships effectively. This article first focuses on the different characteristics of QAR data and uses the Adaptive Noise Ensemble Empirical Mode Decomposition (CEEMDAN) method to obtain more significant potential features of QAR data, solving the problems of mode aliasing and uneven mode gaps that may occur in traditional decomposition methods when processing non-stationary signals. Secondly, a dynamic multidimensional particle swarm optimization algorithm (DMPSO) was constructed using an adaptive adjustment dynamic change method of inertia weight and learning factor, which solved the problem of local extremum and low search accuracy in the solution space that PSO algorithm is prone to during the optimization process. Then, a DMPSO-LSTM aircraft fuel consumption model was established to achieve fuel consumption prediction for three flight segments: climb, cruise, and descent. The final proposed model was validated on real-world datasets, and the results showed that it outperformed other baseline models such as BP, RNN, PSO-LSTM, etc. Among the results, the climbing segment MAE index decreased by more than 40%, the RMSE index decreased by more than 38%, and the R2 index increased by more than 6%, respectively. The MAE index of the cruise segment decreased by more than 40%, the RMSE index decreased by more than 40%, and the R2 index increased by more than 5%, respectively. The MAE index of the descending segment decreased by more than 20%, the RMSE index decreased by more than 30%, and the R2 index increased by more than 5%, respectively. The improved prediction accuracy can be used to implement multi-criteria optimization in flight operations: (1) by quantifying weight–fuel relationships, it supports payload–fuel tradeoff decisions; (2) enhanced phase-specific predictions allow optimized climb/cruise profile selections, balancing time and fuel use; and (3) precise consumption estimates facilitate optimal fuel-loading decisions, minimizing safety margins. The high-precision fuel consumption prediction framework proposed in this study provides actionable insights for airlines to optimize flight operations and design low-carbon route strategies, thereby accelerating the aviation industry’s transition toward net-zero emissions.

Behavioral Biases in the Cryptocurrency Market: A Study on the Impact of Investor Sentiment on Price Anomalies

By combining behavioral finance theory and big data analysis technology, this study explores the mechanism of the impact of investor sentiment on cryptocurrency market price anomalies. Based on the fusion database of traditional exchange historical market data and social media sentiment data, the research team constructed multidimensional sentiment indicators to quantify the emotional fluctuations of market participants. The research design adopts the strict data cleaning process, feature engineering processing, and the hybrid modeling method combining the traditional statistical model and the machine learning algorithm. The empirical results show that extreme optimism or pessimism is significantly associated with abnormal price events, and the predictive ability of the composite sentiment index is better than that of the single volatility index. This research reveals the transmission pathways of cognitive biases such as overconfidence and the anchoring effect in the cryptocurrency market, confirming the significant influence of irrational psychological factors on the price formation of digital assets. The findings not only deepen our understanding of the interaction mechanism between investor psychology and market behavior, but also provide an innovative analytical framework for risk management and quantitative investment strategies in the cryptocurrency field.

Forecasting electricity demand in Türkiye: A comprehensive review of methods, determinants, and policy implications

This review examines the methods, determinants, and forecasting horizons used in electricity demand forecasting in Türkiye. The study investigates how Türkiye's electricity demand is influenced by economic, climatic, socio-demographic, and technological factors, and explores the evolving landscape of forecasting techniques, from traditional statistical models to advanced machine learning and hybrid approaches. The research addresses three key questions: The significant determinants of electricity demand in Türkiye, the most effective forecasting methods, and the application of these insights to improve energy planning and policy development. Through a systematic analysis of peer-reviewed literature, official reports, and case studies, the study reveals the complex interplay of factors affecting electricity demand and the increasing sophistication of forecasting methodologies. Economic growth, industrial production, climate change, urbanization, and technological advancements emerge as primary drivers of demand, while artificial neural networks and hybrid models demonstrate superior forecasting capabilities. The study highlights the importance of integrated modeling approaches, sector-specific strategies, and the incorporation of climate projections in long-term planning. It also emphasizes the need for aligning energy policies with broader economic and environmental objectives. This review provides valuable insights for researchers, policymakers, and industry stakeholders, offering a comprehensive framework for understanding and improving electricity demand forecasting.

A Novel Methodology to Develop Mining Stope Stability Graphs on Imbalanced Datasets Using Probabilistic Approaches

Predicting and analyzing the stability of underground stopes is critical for ensuring worker safety, reducing dilution, and maintaining operational efficiency in mining. Traditional stability graphs are widely used but often criticized for oversimplifying the stability phenomenon and relying on subjective classifications. Additionally, the imbalanced nature of stope stability datasets poses challenges for traditional machine learning and statistical models, which often bias predictions toward the majority class. This study proposes a novel methodology for developing site-specific stability graphs using probabilistic modeling and machine learning techniques, addressing the limitations of traditional graphs and the challenges of imbalanced datasets. The approach includes rebalancing of the dataset using the Synthetic Minority Over-Sampling Technique (SMOTE) and feature selection using permutation importance to identify key features that impact instability, using those to construct a bi-dimensional stability graph that provides both improved performance and interpretability. The results indicate that the proposed graph outperforms traditional stability graphs, particularly in identifying unstable stopes, even under highly imbalanced data conditions, highlighting the importance of operational and geometric variables in stope stability, providing actionable insights for mine planners. Conclusively, this study demonstrates the potential for integrating modern probabilistic techniques into mining geotechnics, paving the way for more accurate and adaptive stability assessment tools. Future work includes extending the methodology to multi-mine datasets and exploring dynamic stability graph frameworks.

Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and K-Means Clustering

Slope deformation poses significant risks to infrastructure, ecosystems, and human safety, making early and accurate predictions essential for mitigating slope failures and landslides. In this study, we propose a novel approach that integrates a fractional-order grey model (FOGM) with particle swarm optimization (PSO) to determine the optimal fractional order, thereby enhancing the model’s accuracy, even with limited and fluctuating data. Additionally, we employ a k-means clustering technique to account for both temporal and spatial variations in multi-point monitoring data, which improves the model’s ability to capture the relationships between monitoring points and increases prediction relevance. The model was validated using displacement data collected from 12 monitoring points on a slope located in Qinghai Province near the Yellow River, China. The results demonstrate that the proposed model outperforms the traditional statistical model and artificial neural networks, achieving a significantly higher coefficient of determination R2 up to 0.9998 for some monitoring points. Our findings highlight that the model maintains robust performance even when confronted with data of varying quality—a notable advantage over conventional approaches that typically struggle under such conditions. Overall, the proposed model offers a robust and data-efficient solution for slope deformation prediction, providing substantial potential for early warning systems and risk management.

Remaining Useful Life (RUL) Prediction Based on the Bivariant Two-Phase Nonlinear Wiener Degradation Process

Recent advancements in science and technology have resulted in products with enhanced reliability and extended lifespans across the aerospace and related sectors. Traditional statistical models struggle to assess their reliability accurately, prompting increased interest in predicting product lifespans during service. These products, characterized by intricate structures and diverse functionalities, exhibit complex, multistage, multiperformance, and nonlinear degradation processes. To address these challenges, this paper proposes a framework for multiperformance, multi-phase Wiener process modeling and reliability analysis. It introduces a two-phase nonlinear Wiener degradation model and identifies change points via the Schwarz information criterion (SIC). The analytical formula for remaining useful life (RUL) is obtained from the concept of the first hitting time (FHT), which considers the stochastic nature of the degradation amount at the change point. The Akaike information criterion (AIC) is then utilized, and an appropriate copula function is chosen to analyze the correlation between two performance indices, given an established complexity with parameters in the degradation model. A two-step method for estimating these uncertain parameters is presented in this paper. Validation through a turbine engine case study underscores its potential to advance reliability theory and engineering practices.

A Low-Cost Antipodal Vivaldi Antenna-Based Peanut Defect Rate Detection System

Peanut quality, with the defect rate as a critical determinant, has a profound impact on its market value. In this study, we introduce an innovative non-destructive evaluation method for peanut defects. Differing from traditional and often expensive or complex detection methods, our approach utilizes a low-cost antipodal Vivaldi antenna, complemented by a custom-designed defect rate detection system. Prior to experimentation, we simulated the antenna and system architecture to ensure their operational efficiency, a step that not only conserves resources but also validates the reliability of subsequent results. We conducted experimental tests on fresh peanut pods, obtaining electromagnetic scattering parameters (S11 and S21 magnitudes/phases within 1–2 GHz) through non-destructive measurements. These parameters were used as input features, while the defect rate served as the output variable. By implementing the XGBoost algorithm, we established predictive models for defect rate quantification (regression) and defect grade classification. In comparison to some traditional statistical models, such as linear regression, which may struggle with non-linear data patterns, XGBoost effectively modeled the complex relationship between the scattering parameters and the defect rate. Experimentally, the regression model achieved an R2 value of 0.8113 for defect rate prediction, and the classification model reached an accuracy of 0.7526 in grading defect severity. The entire device, costing less than USD 50, provides a significant cost advantage over many commercial systems. This low-cost setup enables real-time evaluation of peanut pod defects and efficiently categorizes the defect rate without the time-consuming sample preparation and tiling operations required by traditional image-based inspection methods. As a result, it offers an affordable and practical solution for quality control in peanut production, showing great potential for wide application in the peanut industry.

A Review of Demand Forecasting Models for Indian Coffee Exports: Bridging Gaps and Opportunities

Accurate demand forecasting is crucial for Indian coffee exports, given the sector's vulnerability to market volatility, shifting consumer preferences, and global trade dynamics. While traditional models like ARIMA are widely used, they struggle to address the nonlinear complexities and external factors such as climate variability and trade policies. This review critically synthesizes forecasting methodologies for Indian coffee exports, evaluating their strengths, limitations, and gaps. A systematic review of studies published between 2004 and 2024 was conducted, using databases like Scopus, Springer, and Elsevier, with a focus on relevance to coffee demand forecasting. Findings reveal that while traditional statistical models are effective for linear trends and seasonality, they fail to capture the dynamic nature of the market. Machine learning and hybrid models provide improved accuracy but face scalability and computational challenges. This review highlights gaps in integrating real-time data and domain-specific factors, offering insights for refining forecasting approaches and addressing emerging challenges in the field.

A finger on the pulse of cardiovascular health: estimating blood pressure with smartphone photoplethysmography-based pulse waveform analysis

Smartphone photoplethysmography (PPG) offers a cost-effective and accessible method for continuous blood pressure (BP) monitoring, but faces persistent challenges with accuracy and interpretability. This study addresses these limitations through a series of strategies. Data quality was enhanced to improve the performance of traditional statistical models, while SHapley Additive exPlanations (SHAP) analysis ensured transparency in machine learning models. Waveform features were analyzed to establish theoretical connections with BP measures, and feature engineering techniques were applied to enhance prediction accuracy and model interpretability. Bland–Altman analysis was conducted, and the results were compared against reference devices using multiple international standards to evaluate the method's feasibility. Data collected from 127 participants demonstrated strong correlations between smartphone-derived digital waveform features and those from reference BP devices. The mean absolute errors (MAE) for systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP) using multiple linear regression models were 7.75, 6.35, and 4.49 mmHg, respectively. Random forest models further improved these values to 7.34, 5.79, and 4.45 mmHg. Feature importance analysis identified key contributions from time-domain, frequency-domain, curvature-domain, and demographic features. However, Bland–Altman analysis revealed systematic biases, and the models barely meet established accuracy standards. These findings suggest that while smartphone PPG technology shows promise, significant advancements are required before it can replace traditional BP measurement devices.

Predictive ability of visit-to-visit glucose variability on diabetes complications

BackgroundIdentification of prognostic factors for diabetes complications are crucial. Glucose variability (GV) and its association with diabetes have been studied extensively but the inclusion of measures of glucose variability (GVs) in prognostic models is largely lacking. This study aims to assess which GVs (i.e., coefficient of variation (CV), standard deviation (SD), and time-varying) are better in predicting diabetic complications, including cardiovascular disease (CVD), diabetic retinopathy (DR), and chronic kidney disease (CKD). The model performance between traditional statistical models (adjusting for covariates) and machine learning (ML) models were compared.MethodsA retrospective cohort of type 2 diabetes (T2D) patients between 2010 and 2019 in Ramathibodi Hospital was created. Complete case analyses were used. Three GVs using HbA1c and fasting plasma glucose (FPG) were considered including CV, SD, and time-varying. Cox proportional hazard regression, ML random survival forest (RSF) and left-truncated, right-censored (LTRC) survival forest were compared in two different data formats (baseline and longitudinal datasets). Adjusted hazard ratios with 95% confidence intervals were used to report the association between three GVs and diabetes complications. Model performance was evaluated using C-statistics along with feature importance in ML models.ResultsA total of 40,662 T2D patients, mostly female (61.7%), with mean age of 57.2 years were included. After adjusting for covariates, HbA1c-CV, HbA1c-SD, FPG-CV and FPG-SD were all associated with CVD, DR and CKD, whereas time-varying HbA1c and FPG were associated with DR and CKD only. The CPH and RSF for DR (C-indices: 0.748–0.758 and 0.774–0.787) and CKD models (C-indices: 0.734–0.750 and 0.724–0.740) had modestly better performance than CVD models (C-indices: 0.703–0.730 and 0.698–0.727). Based on RSF feature importance, FPG GV measures ranked higher than HbA1c GV, and both GVs were the most important for DR prediction. Both traditional and ML models had similar performance.ConclusionsWe found that GVs based on HbA1c and FPG had comparable performance. Thus, FPG GV may be used as a potential monitoring parameter when HbA1c is unavailable or less accessible.

Optimal subsampling for double generalized linear models with heterogeneous massive data

. With the development of information technology, massive data under heterogeneous characteristics are generated in the economic, financial, and other fields. Traditional statistical models and existing statistical methods are often inadequate for handling dispersion modeling problems with heterogeneous massive data. In this article, the optimal subsampling of double generalized linear models is studied in heterogeneous massive data environments. Under certain conditions, the optimal subsampling probabilities of the double generalized linear models with heterogeneous data are derived based on the A-optimality criterion and L-optimality criterion, respectively. Furthermore, a two-step algorithm based on uniform sampling is developed, and the asymptotic properties of the subsample estimator from this algorithm are discussed. The results of numerical simulations and a real example show that the algorithm can improve estimation accuracy and decrease computational costs to some extent.

Performance Evaluation of Long Short-Term Memory and Autoregressive Integrated Moving Average Time Series Models for Stock Price Prediction

Abstract: Stock price prediction is a critical task in financial markets, often complicated by the challenges of modeling highly volatile, non-linear, and dynamic time series data. This study evaluates the performance of two prominent forecasting models: Long Short-Term Memory (LSTM) networks, known for their ability to capture long-term dependencies and non-linear patterns, and the Auto-Regressive Integrated Moving Average (ARIMA), a traditional statistical model adept at linear trend modeling. Historical stock price data from the Nigerian Exchange Limited (NGX) was utilized. Both models were implemented in a Python environment, and their predictive accuracy was assessed using performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Additionally, the Diebold-Mariano (DM) test was employed to statistically compare the models’ predictive accuracies. This study highlights the potential of LSTM models for robust stock price forecasting and provides valuable insights for selecting predictive models based on data complexity and market dynamics.

Prediction of Air Quality Index Using Time Series Modelling: A Review Study

Air pollution is a serious environmental issue that affects public health and climate change. Accurate forecasting of the Air Quality Index (AQI) is essential to mitigate its adverse effects and implement effective pollution control measures. This review paper evaluates various time series modelling approaches used for AQI forecasting, including traditional statistical models such as ARIMA and SARIMA, hybrid models that combine statistical and machine learning techniques and deep learning-based approaches such as LSTM and fuzzy time series models. The findings suggest that while ARIMA and SARIMA are effective for short-term forecasting, hybrid models and deep learning techniques provide better accuracy by capturing complex temporal patterns. However, challenges such as data quality issues, computational cost, and regional variations affect the reliability of these models. Future research should focus on developing efficient hybrid approaches to integrate real-time data sources, enhance model interpretability, and improve AQI forecast accuracy. This study provides insights into the strengths and limitations of different forecasting techniques, providing a basis for future advancements in air quality forecasting.

Retail Sales Prediction

Accurate retail sales forecasting is critical for optimizing inventory management, reducing operational costs, and improving strategic decision-making. Traditional statistical models often fail to capture complex consumer behaviors and market fluctuations, leading to inaccurate predictions. With advancements in machine learning (ML), predictive models now offer enhanced accuracy and adaptability in forecasting sales trends. This study evaluates the effectiveness of machine learning algorithms, specifically k-Nearest Neighbor (kNN) regression, Multinomial regression, and Decision Tree with AdaBoost regression, in predicting retail sales. The research assesses model performance using standard evaluation metrics, highlighting the advantages and limitations of each approach. The findings provide valuable insights for retailers, business strategists, and policymakers, offering a data-driven framework for improving sales forecasting and decision-making processes. Keywords: Retail Sales, Forecasting, Machine Learning, Regression Models, Predictive Analytics, Business Intelligence

Selected Topics in Time Series Forecasting: Statistical Models vs. Machine Learning.

Machine learning forecasting methods are compared to more traditional parametric statistical models. This comparison is carried out regarding a number of different situations and settings. A survey of the most used parametric models is given. Machine learning methods, such as convolutional networks, TCNs, LSTM, transformers, random forest, and gradient boosting, are briefly presented. The practical performance of the various methods is analyzed by discussing the results of the Makridakis forecasting competitions (M1-M6). I also look at probability forecasting via GARCH-type modeling for integer time series and continuous models. Furthermore, I briefly comment on entropy as a volatility measure. Cointegration and panels are mentioned. The paper ends with a section on weather forecasting and the potential of machine learning methods in such a context, including the very recent GraphCast and GenCast forecasts.

Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review

ObjectivesTo evaluate whether postpartum haemorrhage (PPH) can be predicted using both machine learning (ML) and traditional statistical models.DesignDiagnostic systematic review and meta-analysis of observational and clinical studies, prospectively registered on...

Machine Learning-Based Early Warning of Algal Blooms: A Case Study of Key Environmental Factors in the Anzhaoxin River Basin

Algal blooms are a major risk to aquatic ecosystem health and potable water safety. Traditional statistical models often fail to accurately predict algal bloom dynamics due to their complexity. Machine learning, adept at managing high-dimensional and non-linear data, provides a superior predictive approach to this challenge. In this study, we employed support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN) models to predict the severity of algal blooms in the Anzhaoxin River Basin based on an algal density-based grading standard. The SVM model demonstrated the highest accuracy with training and test set accuracies of 0.96 and 0.92, highlighting its superiority in small-sample learning. The Shapley Additive Explanations (SHAP) technique was utilized to evaluate the contribution of environmental variables in various predictive models. The results show that TP is the most significant environmental factor affecting the algal bloom outbreak in Anzhaoxin River, and the phosphorus management strategy is more suitable for the management of the artificial water body in northeast China. This study contributes to exploring the potential application of machine learning models in diagnosing and predicting riverine ecological issues, providing valuable insights and support for the protection and management of aquatic ecosystems in the Anzhaoxin River Basin.