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This study aims to evaluate the impact of export product concentration on the ecological footprint in the European Union between 1995 and 2018, using the environmental Kuznets curve framework. We utilized the pooled mean group/autoregressive distributed lag approach, which confirmed the environmental Kuznets curve hypothesis within the European Union. In addition, our findings reveal that export concentration has a negative impact on the ecological footprint, whereas energy consumption increases the ecological footprint. To further validate the obtained results, we conducted robustness checks using the fully modified ordinary least squares and dynamic ordinary least squares approaches, which validated the previous outcomes of the pooled mean group/autoregressive distributed lag approach. In the last step of our study, we employed the Dumitrescu–Hurlin causality analysis, which revealed a bidirectional causality involving the ecological footprint and energy consumption. These empirical findings directly result in valuable recommendations for policymakers. [ABSTRACT FROM AUTHOR]

This study evaluates the potential of applying echo state networks (ESN) and autoregression (AR) for dynamic time series prediction of free surface elevation for use in wave energy converters (WECs). The performance of these models is evaluated on time series data at different water depths and wave conditions, including both measured and simulated data with a focus on real-time prediction of ocean waves at a given location without resolving for the surrounding ocean surface, in other words, short-time single-point forecasting. The work presented includes training the models on historical wave data and testing their ability to predict phase-resolved future surface wave patterns for short-time forecasts. Additionally, this study discusses the feasibility of deploying these models for extended time intervals. It provides valuable insights into the trade-offs between accuracy and practicality in the real-time implementation of predictive models for wave elevation, which are needed in wave energy converters to optimise the control algorithm. [ABSTRACT FROM AUTHOR]

AbstractRecord-breaking temperature events are now very frequently in the news, viewed as evidence of climate change. With this as motivation, we undertake the first substantial spatial modeling investigation of temperature record-breaking across years for any given day within the year. We work with a dataset consisting of over sixty years (1960–2021) of daily maximum temperatures across peninsular Spain. Formal statistical analysis of record-breaking events is an area that has received attention primarily within the probability community, dominated by results for the stationary record-breaking setting with some additional work addressing trends. Such effort is inadequate for analyzing actual record-breaking data. Resulting from novel and detailed exploratory data analysis, we propose rich hierarchical conditional modeling of the indicator events which define record-breaking sequences. After suitable model selection, we discover explicit trend behavior, necessary autoregression, significance of distance to the coast, useful interactions, helpful spatial random effects, and very strong daily random effects. Illustratively, the model estimates that global warming trends have increased the number of records expected in the past decade almost two-fold, 1.93 (1.89,1.98) , but also estimates highly differentiated climate warming rates in space and by season. [ABSTRACT FROM AUTHOR]

Evidence suggests that complex micro‐dynamics occurring in daily life underly the development of mental distress. We aimed to (1) study the cross‐lagged association between stressful events and negative affect (NA), (2) show that there is substantial between‐person variability in idiographic associations and (3) show that idiographic associations are indicative of mental health. Experience sampling study assessing perceived stressfulness of events (PSE) and NA four times per day for 2 weeks in a non‐clinical convenience sample (N = 70, mean age = 22.9, 61% female, 69% German). Bivariate vector autoregressive model implemented in dynamic structural equation modelling to model the associations between stressful events and NA and obtain idiographic associations. Stressfulness of events and NA were significantly reciprocally associated. Autocorrelations and cross‐lagged associations from PSE to NA showed substantial variability and were significantly related with trait measures of depression, anxiety, well‐being, and perceived stress. Contrary to expectations, cross‐lagged associations from NA to stressfulness of events were not related to trait mental health. The approach outlined in this article is useful for studying idiographic dynamics in daily life. The findings increase our understanding of micro‐dynamics underlying mental health and individual differences in these processes. [ABSTRACT FROM AUTHOR]

• Long-term dynamics of algal bloom patterns and their causative factors are assessed. • Time-series satellite data are analyzed and modelled using non-parametric methods. • Increased occurrence and reduction in the areal extent of algal blooms are observed. • The relative influence of ocean, atmosphere, and land parameters changes by regions. • Temporal prediction of blooms is possible with a time lag of 2 months. Algal blooms are global phenomena in marine and fresh waters. An understanding of the impacts of climate change and anthropogenic activities on marine algal blooms is essential for protecting marine ecosystems. We have studied the long-term changes in the spatial patterns of algal blooms over the northern Indian Ocean and assessed the influence of the ocean, atmosphere, and land-based processes on them. We have analyzed multi-source satellite data (2003 – 2020) of various parameters such as algal concentration, temperature, salinity, sea level, particulate organic carbon, precipitation, wind speed, and river discharge on an open-source cloud computing platform. The seasonality of variables is assessed using a harmonic model, and their spatial trend is estimated using the non-parametric Mann-Kendall test. The spatial relationship between various geophysical parameters and algal bloom concentration is assessed by the cross-correlation test, and the prediction is done using a multivariate autoregression model. Results suggest that each variable exhibits distinguishable seasonal patterns. Algal bloom duration showed a significant increase over time. However, algal bloom coverage showed a significant decline in the coastal waters in all seasons except post-monsoon. The cross-correlation test unveils that lower temperatures, salinity, and sea level promote algal bloom occurrences in coastal waters rather than open waters. Furthermore, the statistical model with a time lag of 2 months shows better performance for prediction of bloom intensity (r2 = 0.86). [ABSTRACT FROM AUTHOR]

This study evaluates the potential of applying echo state networks (ESN) and autoregression (AR) for dynamic time series prediction of free surface elevation for use in wave energy converters (WECs). The performance of these models is evaluated on time series data at different water depths and wave conditions, including both measured and simulated data with a focus on real-time prediction of ocean waves at a given location without resolving for the surrounding ocean surface, in other words, short-time single-point forecasting. The work presented includes training the models on historical wave data and testing their ability to predict phase-resolved future surface wave patterns for short-time forecasts. Additionally, this study discusses the feasibility of deploying these models for extended time intervals. It provides valuable insights into the trade-offs between accuracy and practicality in the real-time implementation of predictive models for wave elevation, which are needed in wave energy converters to optimise the control algorithm.

The experience of a long-term study of the population dynamics of phyllophagous insects in Krasnoturansky pine forest (the southern part of Krasnoyarsk Krai) is discussed in the paper. Over the course of 40 years (1978–2016), regular counts of the abundance of five species of phyllophages were carried out in five landscape structures. Based on the data obtained, models of population dynamics of these species were proposed. The models were based on the idea of the existence of positive and negative feedbacks in populations. This approach made it possible to ignore the impact of parasites on insects. For calculations, an autoregressive (AR) model was proposed, according to which the current density linearly depends on the densities of previous years. The model coefficients were calculated using data from long-term surveys. It has been shown that most populations are characterized by the influence of the density of the past season on the current density in the form of a positive feedback and the influence on the current density of the density of the previous season in the form of a negative feedback. The characteristics of the stability of population dynamics of individual species in various habitats are considered. Stability margin indicators, widely used in control theory, were used as stability characteristics. It has been shown that the margin of resistance for sawflies (Tenthredinidae Latreille) is slightly greater than for other groups of species, which is consistent with the smaller dispersion of the population sizes of these species during long-term censuses. The use of AR models and characteristics of the stock of stability made it possible to classify landscape structures on the territory of the Krasnoturansky forest according to the risks of outbreaks of mass reproduction in these landscapes.

This study presents a short-term forecast of UT1-UTC and LOD using two methods, i.e. Dynamic Mode Decomposition (DMD) and combination of Least-Squares and Vector Autoregression (LS+VAR). The prediction experiments were performed separately for yearly time spans, 2018-2022. The prediction procedure started on January 1 and ended on December 31, with 7-day shifts between subsequent 30-day forecasts. Atmospheric Angular Momentum data (AAM) were used as an auxiliary time series to potentially improve the prediction accuracy of UT1-UTC and LOD in LS+VAR procedure. An experiment was also conducted with and without elimination of effect of zonal tides from UT1-UTC and LOD time series. Two approaches to using the best steering parameters for the methods were applied:. First, an adaptive approach, which observes the rule that before every single forecast, a preliminary one must be performed on the pre-selected sets of parameters, and the one with the smallest prediction error is then used for the final prediction; and second, an averaged approach, whereby several forecasts are made with different sets of parameters (the same parameters as in adaptive approach) and the final values are calculated as the averages of these predictions. Depending on the method and data combination mean absolute prediction errors (MAPE) for UT1-UTC vary from 0.63 ms to 1.43ms for the 10th day and from 3.07 ms to 8.05ms for the 30th day of the forecast. Corresponding values for LOD vary from 0.110 ms to 0.245 ms for the 10th day and from 0.148 ms to 0.325 ms for the 30th day.

Social processes and social context are increasingly recognized as key factors shaping health-related behaviors and outcomes. One social process that may be acting within social networks is social influence, in which an individual's characteristic (e.g., specific health behavior) is potentially impacted by the corresponding characteristic of connected individuals in the network. In the health services context, healthcare providers who work together and share patients may influence each other through the knowledge transmission or development of clinical practice norms. Although many statistical techniques assume independence of data points, when analyzing data that may reflect social processes acting across a social network, it is imperative to account for the interdependencies (i.e., non-independence) across individuals. In practice, studies account for nonindependence in the context of estimating bivariate relations (correlations or linear regression) using a variety of analytic methods (some of which have previously been shown to yield biased results). To date, it is unclear which methods yield acceptable false positive rates, unbiased coefficient estimates, and acceptable statistical power, because there have been no systematic simulation studies comparing methods for addressing network nonindependence arising from social influence. To address this gap, we compared eight commonly used methods that purport to account for nonindependence using simulated network data. While results indicated that none of the techniques reduced false positive rates to the predicted (nominal) 0.05 level, random sampling of network nodes was the method that yielded the smallest false positive rates, yet came at a price of reduced statistical power. Further methodological development is needed. [ABSTRACT FROM AUTHOR]

Automatic classification of vessel types in the maritime domain is one of the challenging problems due to the complexity of moving patterns in the ocean that are collected by the Automatic Identification System (AIS). In this study, we explore the usability of different patterns extracted from univariate and multivariate autoregressive modeling for classifying ship types. In order to assess the differentiation power of these features we apply different supervised machine learning classification algorithms and assess the performance of trajectory classification of four different vessel types. In addition, we study the effect of region specification for distinguishing the vessels. The proposed approach produced an accuracy of 86% which confirms that the features obtained from autoregression modeling can identify vessel types effectively. In addition, we demonstrate that the performance of classification can be enhanced further by considering the location of movement. [ABSTRACT FROM AUTHOR]

In this article, we develop additive autoregressive models (Add‐ARM) for the time series data with matrix valued predictors. The proposed models assume separable row, column and lag effects of the matrix variables, attaining stronger interpretability when compared with existing bilinear matrix autoregressive models. We utilize the Gershgorin's circle theorem to impose some certain conditions on the parameter matrices, which make the underlying process strictly stationary. We also introduce the alternating least squares estimation method to solve the involved equality constrained optimization problems. Asymptotic distributions of the parameter estimators are derived. In addition, we employ hypothesis tests to run diagnostics on the parameter matrices. The performance of the proposed models and methods is further demonstrated through simulations and real data analysis. [ABSTRACT FROM AUTHOR]

In this contribution two new approaches are applied to predict polar motion and length-of-day. The first one is based on Dynamic Mode Decomposition (DMD), that is purely data-driven and is capable of reconstructing and forecasting time series in one numerical procedure. The other one is based on a vector autoregression of order p – VAR(p), which is a vector counterpart of AR(p) that accounts for an evolution of variables in time and a coevolution with other variables. DMD was applied to polar motion and length-of-day whilst VAR(p) to a joint prediction of polar motion. A prediction experiment concerned 30-day forecast horizon with a 7-day shift. It was performed separately for years 2017–2022 giving 48 predictions within each year. This study uses IERS EOP 14 C04 (IAU2000) as a reference for all computations and a mean absolute prediction error (MAPE) as a measure of prediction quality. For DMD, MAPEs for x coordinate of the pole vary from 0.22–0.30 mas for the 1st day and 6.64–8.56 mas for the 30th day of prediction depending on the year whilst those values vary from 0.20–0.27 mas and 5.27–7.66 mas for VAR(p) based prediction. Corresponding values for y coordinate of the pole vary from 0.15–0.23 mas and 4.27–5.93 mas for DMD, whilst 0.13–0.21 mas and 3.46–3.82 mas for VAR(p). In case of LOD forecast, MAPEs vary from 0.023–0.031 ms for the 1st day and 0.142–0.205 ms for the 30th day depending on the year. [ABSTRACT FROM AUTHOR]

The regression of mutually independent time series, whether stationary or non-stationary, will result in autocorrelation in the random error term. This leads to the over-rejection of the null hypothesis in the conventional t-test, causing spurious regression. We propose a new method to reduce spurious regression by applying the Cochrane-Orutt feasible generalized least squares method based on a bias-corrected method for a first-order autoregressive model in finite samples. This method eliminates the requirements for a kernel function and bandwidth selection, making it simpler to implement than the traditional heteroskedasticity and autocorrelation consistent method. A series of Monte Carlo simulations indicate that our method can decrease the probability of spurious regression among stationary, non-stationary, or trend-stationary series within a sample size of 10-50. We applied this proposed method to the actual data studied by Yule in 1926, and found that it can significantly minimize spurious regression. Thus, we deduce that there is no significant regressive relationship between the proportion of marriages in the Church of England and the mortality rate in England and Wales. [ABSTRACT FROM AUTHOR]

To writing anything on a keyboard at all requires us to know first what to type, then to activate motor programmes for finger movements, and execute these. An interruption in the information flow at any of these stages leads to disfluencies. To capture this combination of fluent typing and typing hesitations, researchers calculate different measures from keystroke-latency data—such as mean inter-keystroke interval and pause frequencies. There are two fundamental problems with this: first, summary statistics ignore important information in the data and frequently result in biased estimates; second, pauses and pause-related measures are defined using threshold values which are, in principle, arbitrary. We implemented a series of Bayesian models that aimed to address both issues while providing reliable estimates for individual typing speed and statistically inferred process disfluencies. We tested these models on a random sample of 250 copy-task recordings. Our results illustrate that we can model copy typing as a mixture process of fluent and disfluent key transitions. We conclude that mixture models (1) map onto the information cascade that generate keystrokes, and (2) provide a principled approach to detect disfluencies in keyboard typing. [ABSTRACT FROM AUTHOR]

As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic. [ABSTRACT FROM AUTHOR]

Abstract Background Automated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI). Semantic ontology can play a crucial role in knowledge representation, data integration, and information retrieval. Methods This study proposes a semantic ontology model to annotate the AI predictions, forecasting outcomes, and personal preferences to conceptualize a personalized recommendation generation model with a hybrid approach. This study considers a mixed activity projection method that takes individual activity insights from the univariate time-series prediction and ensemble multi-class classification approaches. We have introduced a way to improve the prediction result with a residual error minimization (REM) technique and make it meaningful in recommendation presentation with a Naïve-based interval prediction approach. We have integrated the activity prediction results in an ontology for semantic interpretation. A SPARQL query protocol and RDF Query Language (SPARQL) have generated personalized recommendations in an understandable format. Moreover, we have evaluated the performance of the time-series prediction and classification models against standard metrics on both imbalanced and balanced public PMData and private MOX2-5 activity datasets. We have used Adaptive Synthetic (ADASYN) to generate synthetic data from the minority classes to avoid bias. The activity datasets were collected from healthy adults (n = 16 for public datasets; n = 15 for private datasets). The standard ensemble algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) serve as input for the classification models. Subsequently, we re-verify the classifiers on the private MOX2-5 dataset. The performance of the ontology has been assessed with reasoning and SPARQL query execution time. Additionally, we have verified our ontology for effective recommendation generation. Results We have tested several standard AI algorithms and selected the best-performing model with optimized configuration for our use case by empirical testing. We have found that the autoregression model with the REM method outperforms the autoregression model without the REM method for both datasets. Gradient Boost (GB) classifier outperforms other classifiers with a mean accuracy score of 98.00%, and 99.00% for imbalanced PMData and MOX2-5 datasets, respectively, and 98.30%, and 99.80% for balanced PMData and MOX2-5 datasets, respectively. Hermit reasoner performs better than other ontology reasoners under defined settings. Our proposed algorithm shows a direction to combine the AI prediction forecasting results in an ontology to generate personalized activity recommendations in eCoaching. Conclusion The proposed method combining step-prediction, activity-level classification techniques, and personal preference information with semantic rules is an asset for generating personalized recommendations.

The task of detecting vocal abnormalities is characterized by a small amount of available data for training, as a consequence of which classification systems that use low-dimensional data are the most relevant. We propose to use LASSO (least absolute shrinkage and selection operator) and BSS (backward stepwise selection) methods together to select the most significant features for the detection of vocal pathologies, in particular amyotrophic lateral sclerosis. Features based on fine-frequency cepstral coefficients, traditionally used in speech signal processing, and features based on discrete estimation of the autoregressive spectrum envelope are used. Spectral features based on the autoregressive process envelope spectrum are extracted using the generative method, which involves calculating a discrete Fourier transform of the report sequence generated using the autoregressive model of the input voice signal. The sequence is generated by the autoregressive model so as to account for the periodic nature of the Fourier transform. This improves the accuracy of the spectrum estimation and reduces the spectral leakage effect. Using LASSO in conjunction with BSS allowed us to improve the classification efficiency using a smaller number of features as compared to using the LASSO method alone.

This study investigates the influential economic factors impacting auto insurance premiums and explores the potential for a long-term equilibrium relationship. Monthly data from 25 branches of the National Insurance Company in the regional agency of Ouargla, Algeria, is analyzed using descriptive and case study methods. Through a thorough trend analysis and statistical model formulation, including co-integration tests, stability tests, and regression analyses, we assess the relationship between variables and the impact of independent variables on the dependent variable. The results reveal a statistically acceptable yet weak short-term connection, lacking evidence of common integration. This research provides insights into the complex dynamics of insurance premiums and compensation relationships, suggesting nuanced connections and guiding recommendations for adaptable premium structures and responsive strategies.

Long-span single-pier rigid frame bridge may experience excessive bearing displacement under temperature variation, which can result in structural deformation or instability, causing significant engineering accidents and immeasurable losses on society. This paper proposes a spatial-temporal nonlinear modeling method for temperature and temperature-induced bearing displacement (TIBD) of long-span single-pier rigid frame bridge based on DCNN-LSTM network with elastic modulus fusion, relying on the monitoring data of the Second Yangtze River Bridge in Wuhan. This framework introduces a dynamic autoregressive method for model inference to improve the prediction accuracy of the model. This study addresses three major issues: Insufficient rational selection of temperature characteristic values; Inadequate research on correlation between structural temperature and TIBD for the bridges; Poor prediction accuracy of temperature-TIBD regression models. The method proposed in this study achieves prediction accuracy of up to 99.8 % for TIBD, and this model is not affected by seasonal variation.

Subsurface earth models (referred as geomodels) are crucial for characterizing complex subsurface systems. Multiple-point statistics is commonly used to generate geomodels. In this paper, a deep-learning-based generative method is developed as an alternative to traditional geomodel generation procedure. The generative method comprises two deep-learning models, namely hierarchical vector-quantized variational autoencoder (VQ-VAE-2) and PixelSNAIL autoregressive model. Based on the principle of neural discrete representation learning, the VQ-VAE-2 learns to massively compress the geomodels to extract the low-dimensional, discrete latent representation corresponding to each geomodel. Following that, PixelSNAIL uses deep autoregressive network to learn the prior distribution of the latent codes. For the purpose of geomodel generation, PixelSNAIL samples from the newly learnt prior distribution of latent codes, and then the decoder of the VQ-VAE-2 converts the newly sampled latent code to a newly constructed geomodel. PixelSNAIL can be used for unconditional or conditional geomodel generation. In unconditional generation, the generative workflow generates an ensemble of geomodels without any constraint. On the other hand, in the conditional geomodel generation, the generative workflow generates an ensemble of geomodels similar to a user-defined source image, which ultimately facilitates the control and manipulation of the generated geomodels. To better construct the fluvial channels in the geomodels, perceptual loss is implemented in the VQ-VAE-2 model instead of the traditional 'mean squared error' loss. At a specific compression ratio, the quality of multi-attribute geomodel generation is better than that of single-attribute geomodel generation. [ABSTRACT FROM AUTHOR]

Background: Automated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI). Semantic ontology can play a crucial role in knowledge representation, data integration, and information retrieval. Methods: This study proposes a semantic ontology model to annotate the AI predictions, forecasting outcomes, and personal preferences to conceptualize a personalized recommendation generation model with a hybrid approach. This study considers a mixed activity projection method that takes individual activity insights from the univariate time-series prediction and ensemble multi-class classification approaches. We have introduced a way to improve the prediction result with a residual error minimization (REM) technique and make it meaningful in recommendation presentation with a Naïve-based interval prediction approach. We have integrated the activity prediction results in an ontology for semantic interpretation. A SPARQL query protocol and RDF Query Language (SPARQL) have generated personalized recommendations in an understandable format. Moreover, we have evaluated the performance of the time-series prediction and classification models against standard metrics on both imbalanced and balanced public PMData and private MOX2-5 activity datasets. We have used Adaptive Synthetic (ADASYN) to generate synthetic data from the minority classes to avoid bias. The activity datasets were collected from healthy adults (n = 16 for public datasets; n = 15 for private datasets). The standard ensemble algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) serve as input for the classification models. Subsequently, we re-verify the classifiers on the private MOX2-5 dataset. The performance of the ontology has been assessed with reasoning and SPARQL query execution time. Additionally, we have verified our ontology for effective recommendation generation. Results: We have tested several standard AI algorithms and selected the best-performing model with optimized configuration for our use case by empirical testing. We have found that the autoregression model with the REM method outperforms the autoregression model without the REM method for both datasets. Gradient Boost (GB) classifier outperforms other classifiers with a mean accuracy score of 98.00%, and 99.00% for imbalanced PMData and MOX2-5 datasets, respectively, and 98.30%, and 99.80% for balanced PMData and MOX2-5 datasets, respectively. Hermit reasoner performs better than other ontology reasoners under defined settings. Our proposed algorithm shows a direction to combine the AI prediction forecasting results in an ontology to generate personalized activity recommendations in eCoaching. Conclusion: The proposed method combining step-prediction, activity-level classification techniques, and personal preference information with semantic rules is an asset for generating personalized recommendations. [ABSTRACT FROM AUTHOR]

Fluxes of greenhouse gases (GHG) may be assessed in situ using the eddy covariance method through processing high-frequency measurements of gas concentration and wind speed acquired at certain sites, e.g., carbon measurement test areas of the pilot project of the Ministry of Education and Science of Russia. The measurements commonly come with noise, anomalies, and gaps of various natures. These anomalies result in biased GHG flux estimates. There are a number of empirical and heuristic approaches for filtering noise and anomalies, as well as for gap-filling. These approaches are characterized by many tuning parameters that are commonly adjusted by an expert, which is a limiting factor for large-scale deployment of GHG monitoring stations. In this study, we propose an alternative approach for anomaly detection in high-frequency measurements of GHG concentration and wind speed. Our approach is based on machine learning techniques. This approach is characterized by a lower number of tuning parameters. The goal of our study is to develop a fully automated data preprocessing routine based on machine learning algorithms. We collected the dataset of high-frequency GHG concentration and wind speed measurements from one of the carbon measurement test areas. In order to compare anomaly detection algorithms, we labeled anomalies in a subset of this dataset. We present two approaches for anomaly detection, namely: (a) identification of outliers based on the error magnitude in time series statistical forecasts performed by a machine learning (ML) algorithm; and (b) classification of anomalies using an ML model trained on the labeled dataset of outliers we mentioned above. We compared the approaches and algorithms based on the F1-score metric assessed with respect to an expert-labeled subset of anomalies in GHG concentration and wind speed time series. Within the forecast-error based approach, we trained several ML models: the ARIMA autoregression method, the CatBoost model for autoregression, the CatBoost model for forecasting employing additional features, and the LSTM artificial neural network. Within the supervised classification approach, we tested the CatBoost classification model. We demonstrate that ML models for forecasting deliver a high quality of time series prediction within the autoregression approach. We also show that the anomaly identification method based on the autoregression approach delivers the best quality with the F1-score reaching . [ABSTRACT FROM AUTHOR]

The application of linear interpolation for handling missing hydrological data is unequivocal. On one hand, such an approach offers good reconstruction in the vicinity of last observation before a no-data gap and first measurement after the gap. On the other hand, it omits irregular variability of hydrological data. Such an irregularity can be described by time series models, such as for instance the autoregressive integrated moving average (ARIMA) model. Herein, we propose a method which combines linear interpolation with autoregressive integrated model (ARI, i.e. ARIMA without a moving average part), named LinAR (available at GitHub), as a tool for inputing hydrological data. Linear interpolation is combined with the ARI model through linear scaling the ARI-based prediction issued for the no-data gap. Such an approach contributes to the current state of art in gap-filling methods since it removes artificial jumps between last stochastic prediction and first known observation after the gap, also introducing some irregular variability in the first part of the no-data gap. The LinAR method is applied and evaluated on hourly water level data collected between 2016 and 2021 (52,608 hourly steps) from 28 gauges strategically located within the Odra/Oder River basin in southwestern and western Poland. The data was sourced from Institute of Meteorology and Water Management (Poland). Evaluating the performance with over 100 million assessments in the validation experiment, the study demonstrates that the LinAR approach outperforms the purely linear method, especially for short no-data gaps (up to 12 hourly steps) and for rivers of considerable size. Based on rigorous statistical analysis of root mean square error (RMSE) – expressed (1) absolutely, (2) as percentages and (3) using RMSE error bars – the percentage improvement, understood as percentage difference between RMSE of linear and LinAR interpolations, was found to reach up to 10%. [ABSTRACT FROM AUTHOR]

Three models of abundance dynamics for forest insects that depict the development of outbreak populations were analyzed. We studied populations of the Siberian silkmoth Dendrolimus sibiricus Tschetv. in Siberia and the Far East of Russia, as well as a population of the pine looper Bupalus piniarius L. in Thuringia, Germany. The first model (autoregression) characterizes the mechanism where current population density is dependent on population densities in previous k years. The second model considers an outbreak as analogous to a first-order phase transition in physical systems and characterizes the outbreak as a transition through a potential barrier from a low-density state to a high-density state. The third model treats an outbreak as an effect of stochastic resonance influenced by a cyclical factor such as solar activity and the "noise" of weather parameters. The discussion focuses on the prediction effectiveness of abundance dynamics and outbreak development for each model. [ABSTRACT FROM AUTHOR]

Artificial intelligence is overhauling the publication and textual production. Textual production with Large Language Models (LLMs) is more sophisticated and closer to human knowledge production thanks to its autoregressive nature. AI models by OpenAI such as the Generative Pre-trained Transformer 3 (GPT-3) and its iterations use deep learning to produce more human-like text. Such texts invite all kinds of problems that are not new to the human text-production system. Research shows that the output of such models tends to be biased towards a particular gender, race and ethnicity. This paper explores substantial texts produced by AI including articles, fiction, columns, opinion pieces etc. published in major journals and magazines to understand the nuances of such texts. Moreover, the paper will attempt to understand such features of AI-generated text by presenting the results of a particular set of instructions (prompt) to the LLM. These text outputs will be studied to demonstrate how AI-generated texts pave the way for a "postpost author" era, echoing Barthes' notion of the "Death of the Author". The scope, usability and limitations of the AI's textual production will also be investigated. [ABSTRACT FROM AUTHOR]

We consider a stationary linear model with zero mean. The autoregression parameters, as well as the distribution function (d.f.) of innovations, are unknown. We test symmetry of innovations with respect to zero in two situations. In the first case the observations are a sample from a stationary solution of . We estimate parameters and find residuals. Based on them we construct a kind of empirical d.f. and the omega-square type test statistic. Its asymptotic d.f. under the hypothesis and the local alternatives are found. In the second situation the observations are subject to gross errors (outliers). For testing the symmetry of innovations we again construct the Pearson's type statistic and find its asymptotic d.f. under the hypothesis and the local alternatives. We establish the asymptotic robustness of Pearson's test as well. [ABSTRACT FROM AUTHOR]

The article presents the results of the study of models for short-term forecasting of overall electricity imbalances in the IPS of Ukraine. The analysis of forecasting results obtained using different types of autoregressive models and two forecasting models based on artificial neural networks was performed. Conducted research based on actual data of the balancing market of electric energy of Ukraine showed the effectiveness of using artificial neural networks for the specified task. It is shown that the application of the LSTM (Long short-term memory) artificial neural network model achieves the highest forecasting accuracy for both positive and negative electricity imbalances, respectively, compared to forecasting using autoregressive models. Bibl. 11, fig. 3, table.

Summary: Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet‐connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series. [ABSTRACT FROM AUTHOR]

In this work, panel data that were characterized by features of no first order autocorrelation was modelled using three estimation models: Pool Regression, Fixed Effect, Random Effect models. Panel data like other aspects of econometrics, exploits regression analysis as one of the statistical tools to formulate and illustrate models. The regression analysis requires some assumptions which, if violated, results to one problem or the other. In such case, the Pooling method of estimation remains linear, unbiased and normally distributed but might not be efficient as the estimates of the parameters might become indeterminate, the confidence intervals may be too wide and the standard errors might become large. Simulation studies were carried out at different panel structures and autocorrelation level. The experiment was repeated for 10,000 times and Root Mean Square Error (RMSE) was used to judge the performance of the models. The results from this work showed that for small sample panel structure N = 25, T = 5 and n = 5, irrespective of autocorrelation levels, fixed effect model is preferable at all level. But when moderate for N = 50, T = 10, n = 5, irrespective of autocorrelation level, random effect model is preferred, while for large panel structure for N = 450, T = 30, n = 15, irrespective of autocorrelation level, random effect model is preferred. [ABSTRACT FROM AUTHOR]

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire-prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region-specific seasonality, long-term trends, recent fire observations, and climate drivers representing both large-scale climate variability and local fire weather. We cross-validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near-real-time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

Objectives. The problem of developing a method for calculating small-sized spectral features that increases the efficiency of existing machine learning systems for analyzing and classifying voice signals is being solved.Methods. Spectral features are extracted using a generative approach, which involves calculating a discrete Fourier spectrum for a sequence of samples generated using an autoregressive model of input voice signal. The generated sequence processed by the discrete Fourier transform considers the periodicity of the transform and thereby increase the accuracy of spectral estimation of analyzed signal.Results. A generative method for calculating spectral features intended for use in machine learning systems for the analysis and classification of voice signals is proposed and described. An experimental analysis of the accuracy and stability of the spectrum representation of a test signal with a known spectral composition has been carried out using the envelopes. The envelopes were calculated using proposed generative method and using discrete Fourier transform with different analysis windows (rectangular window and Hanna window). The analysis showed that spectral envelopes obtained using the proposed method more accurately represent the spectrum of test signal according to the criterion of minimum square error. A comparison of the effectiveness of voice signal classification with proposed features and the features based on the mel-frequency kepstral coefficients is carried out. A diagnostic system for amyotrophic lateral sclerosis was used as a basic test system to evaluate the effectiveness of proposed approach in practice. Conclusion. The obtained experimental results showed a significant increase of classification accuracy when using proposed approach for calculating features compared with the features based on the mel-frequency kepstral coefficients.

Bu çalışmada, enflasyon oranlarının giderek hız kazandığı Türkiye’de, sigorta şirketlerinin performanslarının ve prim üretimlerinin enflasyondan nasıl etkilendiği araştırılmıştır. Karlılık oranları için 2006:1-2022:3 ve prim üretimi için de 2008:4-2022:3 dönemlerini kapsayan iki örneklem kullanılarak enflasyonun etkileri incelenmiştir. Analizlerde alternatif zaman serisi teknikleriyle, enflasyon ile karlılık oranları arasındaki kısa ve uzun dönemli nedensellikler araştırılmıştır. Buna ek olarak, başlıca açıklayıcı değişkenin enflasyon oranı olduğu ardışık bağlanımlı regresyon modelleriyle enflasyonun sigorta şirketlerinin performansları ve prim üretimleri üzerindeki etkileri incelenmiştir. Bazı sigorta şirketleri için kimi performans ölçütleri ve prim üretimi açısından enflasyon ile kısa ve uzun dönemli nedensellik ilişkileri belirlenmiştir. Bu ilişkiler genelde enflasyondan prim üretimine doğru tek yönlüdür. Karlılık ölçütleri ve prim üretimi, enflasyondan ya hiç etkilenmemekte ya da pozitif etkilenmektedir. Sigorta şirketlerinin, bir şekilde enflasyonun etkilerinden korunmayı başardığı söylenebilir. Bu durum primlerin enflasyon oranındaki değişmelere göre güncellenmesinden ileri gelebilir. Bir diğer neden de sigorta şirketlerinin etkin bir fon yönetimi ile mali karlılıklarını yükseltmeleri olabilir.

One of the main problems in building energy-efficient and non-resource-intensive decentralized power supply systems in the Arctic zone and regions of the Far North is forecasting the consumption of electrical energy by small northern settlements. Among the existing methods that give an acceptable result in terms of accuracy, one can single out approaches based on the ARIMA econometric method. A method based on the Wold decomposition and correlation functions of the stochastic process is considered, an adaptive model of the difference equation is constructed, which makes it possible to predict energy consumption of the active power of autonomous systems of a small settlement in the interval of 0–4 hours, by reducing the stochastic process to a stationary Markov process with short memory. The purpose of the work is to build a methodology for short-term forecasting of electric energy consumption by autonomous energy systems of small northern settlements, taking into account the specifics of energy consumption in the conditions of the Arctic zone and the Far North, based on a stochastic series of data on electric energy consumed by the settlement over the previous period. Methods: approach to obtaining a short-term forecast of electricity consumption by autonomous energy systems of small northern settlements based on the ARIMA econometric method. In this case, a difference equation is constructed for the deterministic and random components of the available stochastic series of energy consumption; Wold decomposition and correlation functions of the energy consumption are used. To adapt the model, the studied stochastic process is reduced to a Markov process with a short memory. To do this, it is necessary to use a difference operator, which reduces the relative contribution of consumption deterministic component. Results. Based on Wold decomposition and correlation functions, it was possible to obtain a model that gives a short-term forecast of active power consumption for a lead time of 4 hours. Conclusions. Based on the Wold decomposition and the correlation functions of the stochastic process, the authors of the work managed to obtain an adaptive model of the difference equation, which makes it possible to predict active power consumption of autonomous systems of a small settlement with a lead time of 4 hours. The stationarity of the random process was carried out by introducing a difference operator of the first order, which makes it possible to reduce the relative contribution of the deterministic component of the stochastic series. The authors managed to reduce the process to a stationary Markov process with a short memory. The expansion coefficients of the difference equation were estimated by solving a nonlinear equation, which consists in finding the global maximum of the likelihood function. The constructed 90 % probabilistic boundaries allow us to talk about a satisfactory adjustment of the adaptive parameters of the difference equation for predicting the system. The result of forecast modeling with four-hour lead time shows good agreement with experiment.

Movement of soil and associated landslides frequently occur in hilly areas. Regular monitoring, accurate prediction, and timely alerting of people about soil movements on hills susceptible to landslides are essential due to the potential destruction to life and property. A more recent strategy for predicting soil movement is the use of machine learning (ML) models. Different univariate and multivariate ML models have been proposed in the literature. However, evaluating these univariate and multivariate approaches in predicting real-world landslides have received less attention. This paper’s primary goal is to develop and compare the ability of univariate and multivariate ML models (Autoregression (AR), Seasonal Autoregressive Integrated Moving Average (SARIMA), Sequential Minimal Optimization regression (SMOreg), Multilayer Perceptron (MLP), and Long-short Term Memory (LSTM)) to predict movements at a real-world landslide. The case study used for the analysis in this paper is the Tangni landslide in India. This study makes use of weekly averaged soil movement data collected from June 2012 to December 2013 (78 weeks) at the Tangni landslide site. The dataset comprises measurements from five sensors. To calibrate the parameters in each model, we divided the collected data into a training dataset (first 62 weeks) and a test dataset (last 16 weeks). Performance analysis of the models utilized Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared values. The training results revealed that the univariate AR model demonstrated the best performance, achieving an RMSE of 0.149 degrees and an R-squared value of 0.572. The univariate SMOreg model obtained the second-best performance with an RMSE of 0.336 degrees and an R-squared value of 0.582. However, on the test dataset, the multivariate SARIMAX model outperformed the other models, achieving an RMSE of 0.351 degrees and an R-squared value of 0.769. The univariate SARIMA model also performed well with an RMSE of 0.356 degrees and an R-squared value of 0.741. The findings of this study can have significant implications in the field of landslide prediction and prevention. The results indicate that the multivariate SARIMAX model, the most accurate in predicting soil movements, can aid in developing early warning systems against landslides in hilly areas.

The article is dedicated to the problem of recovering gaps in data series of experimental long-term continuous high-frequency observations of carbon dioxide concentration and air temperature. The study was conducted using the observation results from an automatic eco-climatic station located in a tropical monsoon forest in southern Vietnam (Dong Nai biosphere reserve). Gaps in observation series are, as a rule, random and caused by technical malfunctions of the instrumentation. Accurately recovered observation series allow for the assessment of the temporal variability of observed parameters on different time scales. In the scope of this study, options for recovering the continuity of time series based on mathematical statistics methods—autoregression (ARIMA) and the linear prediction method—have been considered. A comparative analysis of the accuracy of gap recovering using different methods is provided. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class's main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach's advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions. [ABSTRACT FROM AUTHOR]

In the linear regression model with possibly autoregressive errors, we construct a family of nonparametric tests for significance of regression, under a nuisance autoregression of model errors. The tests avoid an estimation of nuisance parameters, in contrast to the tests proposed in the literature. A simulation study illustrate their good performance. [ABSTRACT FROM AUTHOR]