Statistical Forecasting Research Articles

Statistical Data-Driven Modelling and Forecasting: An Application to COVID-19 Pandemic

Statistical Data-Driven Modelling and Forecasting: An Application to COVID-19 Pandemic

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features. We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly available neonatal electroencephalogram (EEG) datasets from Cork University and Helsinki University Hospitals. We assessed the performance of Neo-GB at providing dynamic and static forecasts with diagnostic performance metrics and incident/dynamic area under the receiver operating characteristic curve (iAUC) analyses. Model explanations were performed to assess contributions of QEEG features and channels to model predictions. The UCSF dataset included 60 neonates with HIE (30 with seizures). In subject-level static forecasting at 30 min after EEG initiation, baseline Neo-GB without time-dependent features had an area under the receiver operating characteristic curve (AUROC) of .76 and Neo-GB with time-dependent features had an AUROC of .89. In time-dependent evaluation of the initial seizure onset within a 24-h seizure occurrence period, dynamic forecast with Neo-GB demonstrated median iAUC = .79 (interquartile range [IQR] .75-.82) and concordance index (C-index) = .82, whereas baseline static forecast at 30 min demonstrated median iAUC = .75 (IQR .72-.76) and C-index = .69. Model explanation analysis revealed that spectral power, PMA, RQA, and cord blood gas values made the strongest contributions in driving Neo-GB predictions. Within the most influential EEG channels, as the preictal period advanced toward eventual seizure, there was an upward trend in broadband spectral power. This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.

Comparing statistical forecasting methods for modelling respiratory virus healthcare demand

Abstract Background Emergency departments in the United Kingdom are under sustained pressure with rising demand for healthcare, gradually decreasing the functionality of the NHS. Forecasts can aid management in the allocation of appropriate healthcare supplies and staffing needs in advance of an expected surge, as well as allow stakeholders to assess most likely outcomes and potential worst-case scenarios. This is especially valuable during the seasonal waves of respiratory infections. Respiratory viruses are usually concentrated in a 6-8 week seasonal surge during the winter period, creating large pressure on the health system. Current forecast models’ performance depreciates significantly after 1 week. This analysis will forecast 6 weeks of respiratory health admissions, comparing traditional and newly emerging forecasting methods. Methods Using 20 years of English hospital admissions data, we evaluated both within season training models (2-20 weeks) and longer term seasonal models (1-10 years), to forecast 6 weeks of hospital admissions. Respiratory admissions were defined according to ARI/ILI ICD10 codes assigned in hospital, stratified by age and region. Models were iteratively constructed, trained and probabilistic forecasts were evaluated across the respiratory season of September to April using Weighted Interval Score. Averages of each model across iterations were generated for comparison. Results For non-seasonal models, shorter model training lengths created better forecast models for 6 weeks. For seasonal models, optimal training length varied across model complexity. Simple models performed best at forecasting 1 week, however performance deteriorated the most as the forecast window increased. Overall, the Prophet model performed best on average at forecasting 6 weeks across all scenarios. Conclusions Applying our framework for optimal model selection in forecasting analysis will help to inform better practice for future health demand modelling. Key messages • This analysis will inform better practice for future health demand forecasting 6 weeks in advance, allowing for preparation of resources for seasonal surges in healthcare demand. • This analysis suggests using models with modest levels of increased complexity over simpler models could significantly improve forecast performance in the short-medium term.

Research on Nanyang City's Industrial Development Based on Statistical Analysis and ARIMA Forecasting

In recent years, Nanyang City has focused on balancing rapid industrial growth with sustainable development by integrating traditional industries with emerging high-tech sectors to enhance competitiveness and reduce environmental impacts. This study examines the industrial development of Nanyang City using statistical analysis and ARIMA forecasting. Key indicators such as GDP, number of enterprises, employees, and industrial pollution were sourced from the Nanyang Statistics Bureau, preprocessed for quality and consistency, and normalized. The ARIMA model, following stationarity tests and optimal parameter identification, predicts a slight GDP decline in 2023 and, a recovery in 2024, but slower growth by 2027. Environmental indicators were evaluated using Pearson correlation coefficients and the Analytic Hierarchy Process, revealing volatility in green development. The study suggests that Nanyang should strengthen industrial chain integration and technological innovation to enhance competitiveness and environmental performance.

A-197 Enhancing Patient Safety and Workforce Sustainability Through ASCP BOC Credentials

Abstract Background The global healthcare system faces a critical shortage of medical and public health laboratory professionals, hindering patient care with diagnosis and treatment delays. The American Society for Clinical Pathology (ASCP), Board of Certification (BOC) credentials are recognized as a key solution to uphold excellence in lab medicine and pathology, delivering precise test results crucial for 70% of medical decisions. This study investigates how ASCP BOC credentials enhance lab management, bolster patient safety, tackle workforce issues, and foster professional growth amidst evolving healthcare needs. Methods This investigation analyzed the impact of BOC credentials via a comprehensive review of BOC records, certification of over 625,000 professionals, and credential attainment in 117 countries. It evaluated the credentials' effectiveness in ensuring competency, regulatory compliance, and professional development. Additionally, it assessed the BOC’s contribution to mitigating workforce shortages, referencing the 2021 ASCP Wage Survey, 2022 ASCPi Survey, and Bureau of Labor Statistics forecasts, and examined their role in promoting inclusion, diversity, equity, and access (IDEA) within the laboratory profession. Results BOC credentials have significantly contributed to the competence and professionalism of the global laboratory workforce. They are recognized and endorsed by various international accreditation entities like ANAB, the Dubai Health Authority, and others, promoting competency-based examinations worldwide. BOC credentials are being embraced in various regions, including the Americas, Asia, and the Middle East & North Africa, helping to standardize laboratory practices to meet international safety and accuracy benchmarks such as ISO 15189 as prescribed by JCI and CAP. Furthermore, BOC credentials are now recognized in hundreds of countries around the world. In the Philippines, for instance, 35% of respondents to the 2022 BOC ASCPi Survey found employment outside of the Philippines, and 51% attributed their hiring to their BOC credential. Credentialing enhances test reliability, builds trust, and ensures regulatory compliance. BOC credentials and the Credential Maintenance Program (CMP) help public health lab workers meet the CDC's 15 domain areas. They also improve workforce dynamics by providing career advancement and professional development opportunities, attracting and retaining talent. In fact, a 2020 ASCP cross-sectional survey revealed that successful laboratories are characterized by their implementation of strategies including ongoing professional development (such as CMP), and the establishment of career advancement routes tied to credentials. Conclusions BOC credentials play a crucial role in improving the standards of laboratory medicine and pathology, significantly affecting laboratory functionality and patient safety. They help alleviate global workforce shortages by certifying laboratory professionals' expertise and practical skills as a reliable foundation, enhancing workplace culture, and encouraging ongoing professional growth. Furthermore, BOC credentials are essential in promoting IDEA within the lab workforce. The importance of these credentials is underscored by the variety of disciplines covered in the ASCP BOC examination catalog, contributing to a sustainable, robust, and diverse global healthcare system. Future efforts should aim to expand these credentialing programs and use them to further enhance patient care quality, laboratory efficiency, and the durability of the workforce amidst changing healthcare demands.

Evaluating the fidelity of statistical forecasting and predictive intelligence by utilizing a stochastic dataset

Evaluating the fidelity of statistical forecasting and predictive intelligence by utilizing a stochastic dataset

Повышение ВРП экономики Иркутской области на основе роста уровня энергетической эффективности регионального промышленного комплекса

Energy efficiency of fuel and energy consumption is an important indicator of regional economy. The article provides an empirical study of electricity consumption, gross regional product, and electricity consumption per capita in Russia’s regions in 2021. The analysis focused on the role of the Irkutsk Region in the development of Russia’s energy balance and economy. The authors mapped the electric intensity as part of gross regional product and found out that it is very low in the Irkutsk Region, despite the fact that this region is one of the largest consumers of electricity in Russia. The analysis revealed a national need for a better theoretical basis in energy efficiency. The research featured the increase in economic indicators of the Irkutsk Region in conditions of leveling the regional energy efficiency to average indicators. The authors used the methods of statistical research, expert assessments, modeling, and forecasting. They analyzed electric intensity by type of economic activity in various regions of the Siberian Federal District. As a result, they associated the low electric intensity of the gross regional product in Irkutsk with its industrial sector, which uses a lot of electricity and provides a significant share of the gross regional product. They modelled some changes in the volumes of Irkutsk’s gross regional product with an increase in energy efficiency in the industrial power consumption sector. As a result, the gross regional product increased by 57%, rising from 1,268 billion rubles to 2,000 billion rubles. The result was achieved by an increase in the industrial sector from 494 billion rubles to 1,226 billion rubles.

A Review of 15 Years of Public Earthquake Forecasting in Aotearoa New Zealand

Abstract New Zealand entered a period of increased seismic activity in 2003, which, to date, has included around 20 large or damaging earthquakes. Building on decades of forecast model research and including research into formal model evaluation, GNS Science, the governmental institution tasked with providing natural hazards information, began issuing public earthquake forecasts following the 2009 Mw 7.2 Darfield earthquake. This article provides a review of the public earthquake forecasting methods and outcomes since that time. Initially the release of forecasts was motivated by the scientific team, but interest, use and understanding quickly rose in the public, government, and private sectors which led to regular refinement and additions to the type of forecast information provided. The basic tenet of forecasting has remained the same, with the forecasts being used to inform decisions requiring consideration of timeframes from days to decades. The operational models were all based on the previous 10–20 years of research in model development and testing; this included the development of methods for combining multiple models into a single hybrid model that typically provides increased statistical forecast skill. The hybrid models combine models from three classes of time horizons: (1) short-term aftershock clustering based on the epidemic-type aftershock sequence class of models, (2) medium-term clustering with time horizons of years to decades, and (3) long-term models with time horizons of decades to nominally time independent. Forecasts have been issued in response to 14 large or significant earthquakes with the forecasts and accompanying information regularly updated on the GeoNet webpages. An important component to the successful deployment and uptake of the models was the foundation laid in the previous decades in model development and testing. This provided understanding and confidence in the models’ ability to provide useful forecasts for response and recovery decisions.

A Markov model approach for statistical tropical cyclone wind radii baseline forecasts

Abstract A skill baseline for five-day, 34-, 50-, and 64-knot (1 kt = 0.514 m s−1) tropical cyclone (TC) wind radii forecasts is described. The Markov Model CLiper (MMCL) generates a sequence of 12-h forecasts out to a forecast length limited only by the length of the forecast track and intensity. The model employs a climatology of TC size based on infrared satellite imagery, a Markov chain, and a basin-specific drift. MMCL uses the initial wind radii and initial forecast track and intensity as input. Unlike the previously developed wind radii climatology and persistence model (DRCL) that reverts to a climatological size and shape after approximately 48 h, MMCL retains more of its initial size and asymmetry and is likely more palatable for use in operational forecasting. MMCL runs operationally in the western North Pacific basin, the North Indian Ocean, and the Southern Hemisphere for the Joint Typhoon Warning Center (JTWC) in Pearl Harbor, Hawaii. This work also describes the development of Atlantic and eastern North Pacific versions of MMCL. MMCL’s formulation allows unlimited extension of forecast lead time without reverting to a generic climatological size and shape. Independent forecast comparisons between MMCL and DRCL for the 2020–2022 seasons demonstrates that MMCL’s mean absolute errors are generally smaller and biases are closer to zero in North Atlantic, and eastern North Pacific basins, and in the Southern Hemisphere. This validation includes a few example forecasts and demonstrates that MMCL can be used both as a baseline for assessing wind radii forecast skill and operational use.

Blood pressure variability in elderly and senior hypertensive patients

Background. In recent years the deterioration of the demographic situation has been noted in most countries of the world that is due to the steady increase in population of arterial hypertension and the aging of the population. According to the statistical forecast of the UN by 2025, the number of people over 60 will exceed 1 billion which is 15% of the entire global population. In Ukraine, according to epidemiological studies, the prevalence of hypertension among circulatory system diseases in adults (18 years and older) is 46.8% and almost half of patients with circulatory system diseases have elevated blood pressure. In patients aged 65 years and older, the prevalence of hypertension varies in the range of 53-72%. It has long been proven that the risk of cardiovascular and cerebrovascular complications in hypertensive patients depends not only on the absolute blood pressure level but also on fluctuations in blood pressure over different periods that is blood pressure variability. Blood pressure is not a static parameter but rather undergoes continuous fluctuations over time due to the interaction between environmental factors and behaviour on the one hand and the internal regulatory mechanisms of the cardiovascular system on the other hand. Elevated blood pressure may indicate cardiovascular dysregulation and itself may be a cardiovascular risk factor associated with increased all-cause mortality and cardiovascular mortality, stroke, coronary artery disease, heart failure, end-stage renal disease and incidence of dementia. Aim. The study aimed to improve the system of prevention and diagnosis of elderly and senior hypertensive patients at the ambulatory-polyclinic stage due to the study of the contribution of blood pressure variability. Materials and methods. The group of examinees was formed taking into account 27 elderly and senile hypertensive patients including those combined with coronary artery disease. For this purpose, ambulatory blood pressure monitoring was used. Results. Systolic blood pressure variability (SD) in hypertensive patients was higher than in the control group (p<0.001). So, during the study, SD was 17.9±7.1 mm Hg. while in the control group 12.1±2.6 mm Hg. The frequency of high blood pressure variability detection in elderly and senile hypertensive patients was 51.9% (n=14). High blood pressure variability compared to low blood pressure variability and control was associated with a more pronounced systolic blood pressure variability (during the active monitoring period), and it was equalled 23.7±5.2 mm Hg compared to 14.8±6.5 mm Hg (p<0.001) and 12.1±2.6 mm Hg. (p<0.001). In elderly and senile hypertensive patients with high and low blood pressure variability, the non-dipper group prevailed over the dipper group in the structure of daily blood pressure rhythm (71.4% vs. 28.6% and 84.6% vs. 15.4% respectively). According to the ambulatory blood pressure monitoring data high blood pressure variability group compared to low blood pressure variability and controls was characterised by higher average values of the number of blood pressure indicators. In the group of high blood pressure variability patients, the average daily systolic blood pressure exceeded the control value by 10% (р<0.05). The daily index of systolic blood pressure in the group of high blood pressure variability patients was 111% higher than in patients with low blood pressure variability (p<0.05). The daily index of diastolic blood pressure in the group of high blood pressure variability patients was 140% higher than in patients with low blood pressure variability (p<0.05). The daily index of average blood pressure in the group of high blood pressure variability patients was 191% higher than in patients with low blood pressure variability (p<0.01). Conclusions. 1. High blood pressure variability has occurred in 16 out of 27 (51.9%) hypertensive elderly and senile patients. 2. The non-dipper group has predominated in the structure of blood pressure diurnal rhythm in elderly and senile hypertensive patients with high and low blood pressure variability patients. _________________________________________________________________________________________ Keywords: arterial hypertension; essential hypertension; blood pressure variability; high blood pressure variability; low blood pressure variability; elderly and senile hypertensive patients

Statistical seasonal forecasting of tropical cyclone landfalls on Taiwan Island

Forecasting tropical cyclone (TC) activities has been a topic of great interest and research. Taiwan Island (TW) is one of the key regions that is highly exposed to TCs originated from the western North Pacific. Here, the authors utilize two mainstream reanalysis datasets for the period 1979–2013 and propose an effective statistical seasonal forecasting model—namely, the Sun Yat-sen University (SYSU) Model—for predicting the number of TC landfalls on TW based on the environmental factors in the preseason. The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January, the 300-hPa specific humidity over the open ocean southwest of Australia in January, the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March, and the sea surface temperature in the South Indian Ocean in April, are the most significant predictors. The correlation coefficient between the modeled results and observations reaches 0.87. The model is validated by the leave-one-out and nine-fold cross-validation methods, and recent 9-yr observations (2014–2022). The Antarctic Oscillation, variabilities of the western Pacific subtropical high, Asian summer monsoon, and oceanic tunnel are the possible physical linkages or mechanisms behind the model result. The SYSU Model exhibits a 98% hit rate in 1979–2022 (43 out of 44), suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.摘要本文利用1979–2013年的两个主流再分析数据集, 提出了一个中山大学 (SYSU) 热带气旋统计季节预报模型, 基于4个季前环境因子对登陆台湾岛的热带气旋数量进行预报. 模型通过了留一法, 九折交叉验证法和近9年观测数据 (2014–2022) 的验证, 模型结果与实际观测的相关系数达0.87. 南极涛动, 西太平洋副热带高压变化, 亚洲夏季风和海洋通道是模型潜在的物理联系或机制. SYSU模型在1979–2022年期间的预报准确率为98%, 表现出其业务应用价值.

Integrating historical storm surge events into flood risk security in the Copenhagen region

Rapid urbanisation along the coasts of the world in recent decades has increased their vulnerability to storm surges, especially in response to mean sea level rise. The unique geographical and social conditions of Copenhagen, a major European coastal city, have prompted urban expansion along Køge Bay to the south of the city. However, this new urbanisation area is confronted with the common obstacle of developing a coastal defence strategy, i.e., the lack of long-term observational data required to determine a reliable storm surge protection level. This study aims to address this issue by developing a framework that integrates historical records of extreme storm surge events into coastal defence strategies, using Copenhagen as a case study. We propose a four-step work framework, including (1) Data collection and analysis: We collected and analysed data from neighbouring cities and used modelling and reanalysis data sets. By combining these sources, we aim to reconstruct historical time series for the study site dating back to 1836. This extended information set enhances our understanding of past storm surge events. (2) Statistical modelling and forecasting: Using Bayesian statistical methods, we fitted the historical storm surge data to appropriate probability distributions. This enabled us to generate probabilistic forecasts of storm surge magnitudes, providing insight into the likelihood of future events and their potential impacts on the coastal area. (3) Sensitivity analyses: We performed sensitivity experiments using Markov chain Monte Carlo (MCMC) methods to identify the most influential parameters, such as thresholds, that affect storm surge levels. This analysis improved our understanding of the key drivers of storm surge events and their uncertainties, further informing coastal defence strategies. (4) Expert judgement and risk management: Expert judgements are implemented to establish the necessary security level to manage flood risks in the city. This helps to ensure that high-impact, low-probability events are adequately considered in risk management efforts. Following this framework, we can develop a comprehensive understanding of storm surge risks in the urbanised region south of Copenhagen and use historical data to inform coastal defence strategies. This study emphasises the importance of incorporating long-term observational data and expert insights to improve the resilience of coastal cities facing the challenges of urbanisation and climate change.

Improving the Predictability of the US Seasonal Surface Temperature With Convolutional Neural Networks Trained on CESM2 LENS

AbstractTo better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales.

Integrated models for carbon emission prediction: Combining epidemiology models and statistical forecasting

The topic of our article is Integrated Models for Carbon Emission Prediction: Combining Epidemiology Models and Statistical Forecasting. In this work, we need to combine various epidemiological models from different perspectives, including evolution, application and statistics, to simulate and predict carbon emissions and obtain the results we need. The following content is about the results we have obtained, and its significance lies in predicting and analyzing when and how much carbon emissions will peak in various possible scenarios. Predict the most likely peak time and peak volume from a regional and industry perspective, providing decision-making basis and reference for scientifically and reasonably formulating emission reduction policies. Also, some application in real life of the integrated models is shown in this paper. In addition, the limitations and challenges we face are provided in the article, we are able to consider how to improve the prediction by analyzing these limitations. This article includes a case study about energy which allows us to better have a more comprehensive and vivid image and understanding of the models. All the actions in this work will be meaningful in our life because carbon play an important role in environment like greenhouse effect and biological carbon cycle.

Improvement of methods of rational use in conditions of land degradation in the Almaty region, Karasai district

The study was aimed at identifying the potential of modern methods of rational land use in the Karasai district of the Almaty region of Kazakhstan, taking into account their intensive degradation. The research methodology was represented by the methods of statistical observation, comparison, analytical-structural grouping and forecasting. The priority goals of modernisation of agriculture of the Republic in the technological aspect have been analysed. It was established that innovative approaches to land use have the potential to increase the level of efficiency of the agrarian sector, improve the state of local and regional landscape. The concept of improving the state of degraded lands, including a system of management measures and practical activities, has been developed. It was proved that it should be based on the synergy of economic efficiency and environmental safety, with the mandatory introduction of modern innovative technological approaches. The effectiveness of methods of rational use of degraded lands as an effective tool for the transformation of the agrarian sector has been determined. It was proposed to intensify the development of organic agricultural production, which is positioned as the gentlest for degraded landscapes. It was proved that the implementation of sustainable management of landscape complexes in the innovative context implies the use of information monitoring technology, which anticipates the diagnosis, genesis and forecasting of the state of the studied ecosystems. Such measures will make it possible to develop programmes for the restoration of ecological functions of natural landscapes, which is an integral part of sustainable development programmes. Actualised the situation in the context of ecologisation of land use in Kazakhstan, predicted further destruction of ecosystems of agrarian landscapes in case of use of aggressive methods of soil cultivation. The necessity of introduction of improved methods of rational land use is substantiated, and the specificity of the introduction of organic and biological technologies of agricultural production is outlined, with an indication of tangential risks and challenges in the economic realities of Kazakhstan. The study substantiated that the application of integrated management and ecosystem approach have a synergistic potential to ensure the rational use of land in the Almaty region, the Karasai district in the conditions of their degradation

Lagrangian descriptors with uncertainty

Lagrangian descriptors provide a global dynamical picture of the geometric structures for arbitrarily time-dependent flows with broad applications. This paper develops a mathematical framework for computing Lagrangian descriptors when uncertainty appears. The uncertainty originates from estimating the underlying flow field as a natural consequence of data assimilation or statistical forecast. It also appears in the resulting Lagrangian trajectories. The uncertainty in the flow field directly affects the path integration of the crucial nonlinear positive scalar function in computing the Lagrangian descriptor, making it fundamentally different from many other diagnostic methods. Despite being highly nonlinear and non-Gaussian, closed analytic formulae are developed to efficiently compute the expectation of such a scalar function due to the uncertain velocity field by exploiting suitable approximations. A rapid and accurate sampling algorithm is then built to assist the forecast of the probability density function (PDF) of the Lagrangian trajectories. Such a PDF provides the weight to combine the Lagrangian descriptors along different paths. Simple but illustrative examples are designed to show the distinguished behavior of using Lagrangian descriptors in revealing the flow field when uncertainty appears. Uncertainty can either completely erode the flow3 structure or barely affect the underlying geometry of the flow field. The method is also applied for eddy identification, indicating that uncertainty has distinct impacts on detecting eddies at different time scales. Finally, when uncertainty is incorporated into the Lagrangian descriptor for inferring the source target, the likelihood criterion provides a very different conclusion from the deterministic methods.

Sources of seasonal water supply forecast uncertainty during snow drought in the Sierra Nevada

AbstractUncertainty attribution in water supply forecasting is crucial to improve forecast skill and increase confidence in seasonal water management planning. We develop a framework to quantify fractional forecast uncertainty and partition it between (1) snowpack quantification methods, (2) variability in post‐forecast precipitation, and (3) runoff model errors. We demonstrate the uncertainty framework with statistical runoff models in the upper Tuolumne and Merced River basins (California, USA) using snow observations at two endmember spatial resolutions: a simple snow pillow index and full‐catchment snow water equivalent (SWE) maps at 50 m resolution from the Airborne Snow Observatories. Bayesian forecast simulations demonstrate a nonlinear decrease in the skill of statistical water supply forecasts during warm snow droughts, when a low fraction of winter precipitation remains as SWE. Forecast skill similarly decreases during dry snow droughts, when winter precipitation is low. During a shift away from snow‐dominance, the uncertainty of forecasts using snow pillow data increases about 1.9 times faster than analogous forecasts using full‐catchment SWE maps in the study area. Replacing the snow pillow index with full‐catchment SWE data reduces statistical forecast uncertainty by 39% on average across all tested climate conditions. Attributing water supply forecast uncertainty to reducible error sources reveals opportunities to improve forecast reliability in a warmer future climate.

The Future of Total Elbow Arthroplasty: A Statistical Forecast Model for Germany.

This study provides a statistical forecast for the development of total elbow arthroplasties (TEAs) in Germany until 2045. The authors used an autoregressive integrated moving average (ARIMA), Error-Trend-Seasonality (ETS), and Poisson model to forecast trends in total elbow arthroplasty based on demographic information and official procedure statistics. They predict a significant increase in total elbow joint replacements, with a higher prevalence among women than men. Comprehensive national data provided by the Federal Statistical Office of Germany (Statistisches Bundesamt) were used to quantify TEA's total number and incidence rates. Poisson regression, exponential smoothing with Error-Trend-Seasonality, and autoregressive integrated moving average models (ARIMA) were used to predict developments in the total number of surgeries until 2045. Overall, the number of TEAs is projected to increase continuously from 2021 to 2045. This will result in a total number of 982 (TEAs) in 2045 of mostly elderly patients above 80 years. Notably, female patients will receive TEAs 7.5 times more often than men. This is likely influenced by demographic and societal factors such as an ageing population, changes in healthcare access and utilization, and advancements in medical technology. Our projection emphasises the necessity for continuous improvements in surgical training, implant development, and rehabilitation protocols.

A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts

The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational expenses management. The forecasting methods utilized include statistical techniques (autoregressive integrated moving average [ARIMA], and polynomial regression), machine learning (nonlinear autoregressive neural network [NAR]), deep learning (long short-term memory [LSTM]), hybrid and combination forecasting. With regard to the latter method, our study experiments with four different combining model approaches, including the introduction of an original, novel combining method with the employment of a transcendental LASSO function, which is used to form combinations of forecasts generated by the NAR, ARIMA, and polynomial regression models. The LASSO-based combining model proved superior to all other models, for the majority of forecast error statistics; where the root mean square error (RMSE) and mean absolute percentage error (MAPE) values are 4.5% and 8% respectively lower than the average level of the component model forecasts. The major implications of our empirical findings are that greater accuracy in demand forecasting can be achieved with a combining model approach, rather than reliance on any particular, singular model. Furthermore, given its superior performance, the employment of the studys LASSO-based combining model to forecast electric vehicle demand may lead to optimal firm decision-making over a range of organizational facets, which is predicated on accurate demand function estimation.

Single-instant spatial wave height forecast using machine learning: An image-to-image translation approach based on generative adversarial networks

An innovative single-instant approach for spatial significant wave height forecast based on a Generative Adversarial Networks (GANs) model is presented. The model's most distinctive feature is its ability to provide spatial wave height forecast with only a single snapshot of wind field input, eliminating the need for continuous wind field sequences while simultaneously maintaining high accuracy and enhanced efficiency. The proposed model architecture comprises an image-to-image translation generator and a patch-style discriminator, working in tandem to forecast high-quality wave height data. The model is trained using a 20-year ECMWF Reanalysis v5 (ERA5) wind and wave dataset. A vast sea area covering 15°-40°N, 105°-130°E in West Pacific is used for this study. The presented model is carefully tested against a 10-year period historical dataset and the results are discussed. The model exhibits outstanding overall performances with Mean Absolute Error (MAE) of 0.16 m, Root Mean Square Error (RMSE) of 0.24 m, and Mean Absolute Percentage Error (MAPE) of 13.4 %. The model proves particularly effective in forecasting extreme wave heights during tropical cyclones, capturing the structure of typhoon-driven waves and providing wave heights with favorable accuracy. It is also proficient at delivering satisfactory statistical wave height forecasts for long-term periods.