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Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance. [ABSTRACT FROM AUTHOR]

The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when data is arising from a variety of sources, not necessarily at the same spatial scale, it is possible to specify spatial random effects, or covariate effects, at the areal level, by using a multiple membership principle (MM) (Petrof et al., 2020; Gramatica et al., 2021). A weighted average of conditional autoregressive (CAR) spatial random effects embeds spatial information for a spatially-misaligned outcome and estimate relative risk for both frameworks (areas and memberships). In this paper we investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior, in particular with regard to parameterisation, properness and identifiability. We carry out simulations involving different numbers of memberships as compared to number of areas and assess impact of this on estimating parameters of interest. Both analytical and simulation study results show under which conditions parameters of interest are identifiable, so that we can offer actionable recommendations to researchers. Finally, we present the results of an application of the multiple membership model to diabetes prevalence data in South London, together with strategic implications for public health considerations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new credibility model based on heterogeneous integer-valued self-exciting threshold autoregressive time series, in which the SETINAR(2,1) process is used to fit the claim numbers of policyholders for consecutive periods, and the unobservable heterogeneity is assumed to follow Gamma distribution. We obtain the Bayesian pricing formula for the proposed model and present some numerical examples to illustrate how the claim history affects the future premiums. We also apply the proposed model to a real panel dataset from the Wisconsin Local Government Property Insurance Fund. By comparing with some existing models, we find that our model can exploit the past information more efficiently and has better predictive performance. [ABSTRACT FROM AUTHOR]

To capture the higher-order autocorrelation structure for finite-range integer-valued time series of counts, and to consider the driving effect of covariates on the underlying process, this paper introduces a pth-order random coefficients mixed binomial autoregressive process with explanatory variables. The basic probabilistic and statistical properties of the model are discussed. Conditional least squares and conditional maximum likelihood estimators, as well as their asymptotic properties of the estimators are obtained. Moreover, the existence test of explanatory variables are well addressed using a Wald-type test. Forecasting problem is also considered. Finally, some numerical results of the estimators and a real data example are presented to show the performance of the proposed model. [ABSTRACT FROM AUTHOR]

We propose an autoregressive weighted network (ARWNet) time series forecasting model inspired by the idea of ensemble learning. The model adopts the classical autoregressive analysis to optimize weak learners. Meanwhile, the combined weight optimization method is used to construct an efficient, strong learner. With these methodological foundations, the scalability of the framework is greatly enhanced by the possibility of experimenting with other learners to assist in decision‐making. Machine learning can provide great utility and acceptable cost in the prediction process of electricity transformers. Over the years, many research papers on time series prediction have been reported. This work will focus on the analysis using the potential properties in the series: long‐term, continuity, periodicity, and delay. In our experiments, the ETT‐small dataset is used to compare the prediction accuracy of ARWNet and other mainstream models. All results suggest that the proposed ARWNet model demonstrates strong generalization ability and high predicting accuracy with delay characteristics, which outperform current popular time‐series prediction methods. [ABSTRACT FROM AUTHOR]

This paper constructs a vocal singing training optimization model based on multimedia data analysis and conducts in-depth research and analysis on the optimization of vocal singing training methods using multimedia data analysis system. For the problems of poor real-time and large analysis error of the traditional system, a massive real-time multimedia data analysis system based on DSP is designed, with high-speed DSP as the core architecture, multipoint modeling of the experimenter, and data extraction and fusion processing are completed with model association characteristics as the analysis basis. The experimental results show that the real-time performance and accuracy of the system are improved by 15.14% and 13.42%, respectively. To address the time-space domain consistency problem in traditional time series data forecasting, two high-order time series forecasting algorithms based on tensor decomposition are proposed in this paper: the multilinear orthogonal autoregressive model and the multilinear constrained autoregressive model. Using the local structure information of the data to reduce the dimensionality of the data is more beneficial to extract the true low-dimensional representation of the data. Most current supervised cross-modal retrieval methods use a strict binary label matrix (i.e., 0 and 1); however, the interval between labels 0 and 1 is small, which may increase the risk of classification errors. To solve some of the above existing problems, a two-stage hashing method based on smooth matrix decomposition and label relaxation is proposed, which proposes a novel label relaxation strategy that can adaptively control the interval between label intervals and can reduce the quantization loss by about 5.21%. The multimedia data analysis vocal singing training optimization model designed in this paper can improve enhance students' motivation and accuracy and provide technical support and a rational basis for vocal singing practice and overall musical expression. [ABSTRACT FROM AUTHOR]

In this paper, an exponential autoregressive model for complex time series data is presented. As for estimating the parameters of this nonlinear model, a three-step procedure based on quantile methods is proposed. This quantile-based estimation technique has the benefit of being more robust compared to least/absolute squares. The performance of the introduced exponential autoregressive model is evaluated by means of four established goodness-of-fit criteria. The practical utility of the novel time series model is showcased through a comparative analysis involving simulation studies and real-world data illustrations. [ABSTRACT FROM AUTHOR]

This paper derives a test for deciding whether two time series come from the same stochastic model, where the time series contains periodic and serially correlated components. This test is useful for comparing dynamical model simulations to observations. The framework for deriving this test is the same as in the previous three parts: the time series are first fit to separate autoregressive models, and then the hypothesis that their parameters are equal is tested. This paper generalizes the previous tests to a limited class of nonstationary processes, namely, those represented by an autoregressive model with deterministic forcing terms. The statistic for testing differences in parameters can be decomposed into independent terms that quantify differences in noise variance, differences in autoregression parameters, and differences in forcing parameters (e.g., differences in annual cycle forcing). A hierarchical procedure for testing individual terms and quantifying the overall significance level is derived from standard methods. The test is applied to compare observations of the meridional overturning circulation from the RAPID array to Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Most CMIP5 models are inconsistent with observations, with the strongest differences arising from having too little noise variance, though differences in annual cycle forcing also contribute significantly to discrepancies from observations. This appears to be the first use of a rigorous criterion to decide "equality of annual cycles" in regards to all their attributes (e.g., phases, amplitudes, frequencies) while accounting for serial correlations. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer remains a global burden, currently causing nearly one in six deaths worldwide. Accurate projections of cancer incidence and mortality are needed for effective and efficient policymaking, accurate resource allocation, and to assess the impact of newly introduced policies and measures. However, the COVID-19 pandemic disrupted public health systems and caused a significant number of cancers to remain undiagnosed, thus affecting the quality of official statistics and their usefulness for health studies. This paper addresses this issue by proposing novel cancer incidence/cancer mortality models based on population web-search habits and historical links with official health variables. The models are empirically estimated using data from one of the most vulnerable European Union (EU) members, Romania, a country that consistently reports lower survival rates than the EU average, and are further used to forecast cancer incidence and mortality rates in the country. Research findings have important policy implications, and the novel framework, owing to its generalizability, can be applied to the same task in other countries. Overall, the results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania and thus underline the urgency to change the status quo in the Romanian public-health system. Cancer remains a leading cause of worldwide mortality and is a growing, multifaceted global burden. As a result, cancer prevention and cancer mortality reduction are counted among the most pressing public health issues of the twenty-first century. In turn, accurate projections of cancer incidence and mortality rates are paramount for robust policymaking, aimed at creating efficient and inclusive public health systems and also for establishing a baseline to assess the impact of newly introduced public health measures. Within the European Union (EU), Romania consistently reports higher mortality from all types of cancer than the EU average, caused by an inefficient and underfinanced public health system and lower economic development that in turn have created the phenomenon of "oncotourism". This paper aims to develop novel cancer incidence/cancer mortality models based on historical links between incidence and mortality occurrence as reflected in official statistics and population web-search habits. Subsequently, it employs estimates of the web query index to produce forecasts of cancer incidence and mortality rates in Romania. Various statistical and machine-learning models—the autoregressive integrated moving average model (ARIMA), the Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and a feed-forward neural network nonlinear autoregression model, or NNAR—are estimated through automated algorithms to assess in-sample fit and out-of-sample forecasting accuracy for web-query volume data. Forecasts are produced with the overperforming model in the out-of-sample context (i.e., NNAR) and fed into the novel incidence/mortality models. Results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania by 2026, with projected levels for the age-standardized total cancer incidence of 313.8 and the age-standardized mortality rate of 233.8 representing an increase of 2%, and, respectively, 3% relative to the 2019 levels. Research findings thus indicate that, under the no-change hypothesis, cancer will remain a significant burden in Romania and highlight the need and urgency to improve the status quo in the Romanian public health system. [ABSTRACT FROM AUTHOR]

Forecasting of PM2.5 concentration is a global concern. Evidence has shown that the ambient PM2.5 concentrations are harmful to human health, climate change, plant species mortality, etc. PM2.5 concentrations are caused by natural and anthropogenic activities, and it is challenging to predict them due to many uncertain factors. Current research has focused on developing a new model while overlooking the fact that every single model for PM2.5 prediction has its own strengths and weaknesses. This paper proposes an ensemble framework which combines four diverse learning models for PM2.5 forecasting in Puli, Taiwan. It explores the synergy between parametric and non-parametric learning, and short-term and long-term learning. The feature set covers periodic, meteorological, and autoregression variables which are selected by a spiral validation process. The experimental dataset, spanning from 1 January 2008 to 31 December 2019, from Puli Township in central Taiwan, is used in this study. The experimental results show the proposed multi-model framework can synergize the advantages of the embedded models and obtain an improved forecasting result. Further, the benefit obtained by blending short-term learning with long-term learning is validated, in surpassing the performance obtained by using just single type of learning. Our multi-model framework compares favorably with deep-learning models on Puli dataset. It also shows high adaptivity, such that our multi-model framework is comparable to the leading methods for PM2.5 forecasting in Delhi, India. [ABSTRACT FROM AUTHOR]

Recently, deep neural networks have been widely used to solve financial risk modeling and forecasting challenges. Following this hotspot, this paper presents a mixture model for conditional volatility probability forecasting based on the deep autoregressive network and the Gaussian mixture model under the GARCH framework. An efficient algorithm for the model is developed. Both simulation and empirical results show that our model predicts conditional volatilities with smaller errors than the classical GARCH and ANN-GARCH models. [ABSTRACT FROM AUTHOR]

In this paper, we consider an extension of the classical discrete-time dual risk model, in which the first-order integer-valued autoregressive (INAR(1)) process with Poisson distributed innovations is utilized to fit the temporal dependence between the number of gains for each period. We derive the explicit expression for a function that allows us to find the Lundberg adjustment coefficient and obtain the Lundberg approximation formula for ruin probability. Some numerical examples are provided to illustrate our main results. [ABSTRACT FROM AUTHOR]

This paper proposes a partially periodic oscillation model, which is motivated by time series modelling for COVID-19 daily confirmed cases, in particular, to represent more accurately the dynamic features of the 7-day periodicity. In order to express the phenomenon of the partial 7-day cycle in the COVID-19 data, some partial periodic part is added to a heterogeneous autoregression model. Estimation algorithm based on the least squares errors and regression analysis is provided and parameter estimation consistency is given along with its proof. A Monte-Carlo simulation study is carried out to investigate the finite-sample performance. The proposed model is applied to the COVID-19 daily confirmed cases of the most affected eight countries which posses the partially periodic oscillation. Model criteria such as RMSE, MAE, HMAE, AIC and BIC are compared with other existing models. Efficiency of the model, relative to the benchmark, is evaluated to reveal its better accuracy performance. Out-of-sample forecasting analysis is conducted as well. The novelty is that this work is a challenging trial to identify the partially periodic oscillation of COVID-19 data, without smoothing, as well as the proposed model outperforms the existing time series models in the empirical analysis of the worldwide COVID-19. • A partially periodic oscillation model combined with heterogeneous autoregression is proposed. • The model is applied to the COVID-19 daily confirmed cases in the top eight countries with partially periodic oscillation. • A simple and robust estimation method is developed. • Model criteria measures such as RMSE, MAE, HMAE, AIC and BIC are evaluated and compared with existing models. • Efficiency of the proposed model, relative to benchmark models, is reported as 126%-163% improvement in some countries. [ABSTRACT FROM AUTHOR]

The quantitative prediction of CO2 concentration in the growth environment of crops is a key technology for CO2 enrichment applications. The characteristics of micro/nanobubbles in water make CO2 micro/nanobubble water potentially useful for enriching CO2 during growth of crops. However, few studies have been conducted on the release characteristics and factors influencing CO2 micro/nanobubbles. In this paper, the factors influencing CO2 release and changes in CO2 concentration in the environment are discussed. An autoregressive integrated moving average and backpropagation neural network (ARIMA-BPNN) model that maps the nonlinear relationship between the CO2 concentration and various influencing factors within a time series is proposed to predict the released CO2 concentration in the environment. Experimental results show that the mean absolute error and root-mean-square error of the combination prediction model in the test datasets were 9.31 and 17.48, respectively. The R2 value between the predicted and measured values was 0.86. Additionally, the mean influence value (MIV) algorithm was used to evaluate the influence weights of each input influencing factor on the CO2 micro/nanobubble release concentration, which were in the order of ambient temperature > spray pressure > spray amount > ambient humidity. This study provides a new research approach for the quantitative application of CO2 micro/nanobubble water in agriculture. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM10 concentration forecasting is important for early prevention and in urban development planning, which is crucial for developing cities. This paper presents the development of PM10 forecasting model using nonlinear autoregressive with exogenous input model. METHODS: To improve performance of nonlinear autoregressive with exogenous input model, principal component analysis is used prior to the model for variable selection. The first stage of principal component analysis involves Scree plot, which determines the number of principal components based on explained variance. This is then followed by selecting variables using a rotated component matrix, based on their strength of contribution towards variation of PM10 concentration. To test the model, PM10 data in Kota Kinabalu from 2003 - 2010 was used. Neural network models are developed using this data by varying number of input variables with the inclusion of temporal variables. The developed forecasting models are evaluated using data PM10 in the city from 2011 to 2012. Four performance indicators, namely root mean square error, mean absolute error, index of agreement and fractional bias are reported. FINDINGS: Results from principal component analysis show that five variables including wind direction index, relative humidity, ambient temperature, concentration of nitrogen dioxide and concentration of ozone strongly contribute to the variation of PM10 concentration. By using these variables together with temporal variables as input in the nonlinear autoregressive with exogenous input models, the resultant model shows good forecasting performance, with root mean square error of 7.086±0.873 µg/m3. The selection of significant variables helps in reducing input variables inside the forecast model without degrading its forecast performance. CONCLUSION: This model shows very promising performance in forecasting PM10 concentration in Kota Kinabalu as it requires fewer input variables and does not require variable transformation. [ABSTRACT FROM AUTHOR]

In this paper additive models with p-order autoregressive conditional symmetric errors based on penalized regression splines are proposed for modeling trend and seasonality in time series. The aim with this kind of approach is try to model the autocorrelation and seasonality properly to assess the existence of a significant trend. A backfitting iterative process jointly with a quasi-Newton algorithm are developed for estimating the additive components, the dispersion parameter and the autocorrelation coefficients. The effective degrees of freedom concerning the fitting are derived from an appropriate smoother. Inferential results and selection model procedures are proposed as well as some diagnostic methods, such as residual analysis based on the conditional quantile residual and sensitivity studies based on the local influence approach. Simulations studies are performed to assess the large sample behavior of the maximum penalized likelihood estimators. Finally, the methodology is applied for modeling the daily average temperature of San Francisco city from January 1995 to April 2020. [ABSTRACT FROM AUTHOR]

Exponential smoothing methods are one of the classical time series forecasting methods. It is well known that exponential smoothing methods are powerful forecasting methods. In these methods, exponential smoothing parameters are fixed on time, and they should be estimated with efficient optimization algorithms. According to the time series component, a suitable exponential smoothing method should be preferred. The Holt method can produce successful forecasting results for time series that have a trend. In this study, the Holt method is modified by using time-varying smoothing parameters instead of fixed on time. Smoothing parameters are obtained for each observation from first-order autoregressive models. The parameters of the autoregressive models are estimated by using a harmony search algorithm, and the forecasts are obtained with a subsampling bootstrap approach. The main contribution of the paper is to consider the time-varying smoothing parameters with autoregressive equations and use the bootstrap method in an exponential smoothing method. The real-world time series are used to show the forecasting performance of the proposed method. [ABSTRACT FROM AUTHOR]

The time-inhomogeneous autoregressive model AR(1) is studied, which is the process of the form Xn+1 = αnXn + εn, where αn are constants, and εn are independent random variables. Conditions on αn and distributions of εn are established that guarantee the geometric recurrence of the process. This result is applied to estimate the stability of n-steps transition probabilities for two autoregressive processes X(1) and X(2) assuming that both αn(i), i ∈ {1, 2}, and distributions of εn(i), i ∈ {1, 2}, are close enough. [ABSTRACT FROM AUTHOR]

Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 virus was first registered in Brazil by the end of February 2020. Since then, the country counts over 150000 deaths due to COVID-19 and faces a profound social and economic crisis; there is also an ongoing health catastrophe, with the majority of hospital beds in many Brazilian cities currently occupied with COVID-19 patients. Thus, a Nonlinear Model Predictive Control (NMPC) scheme used to plan appropriate social distancing measures (and relaxations) in order to mitigate the effects of this pandemic is formulated in this paper. The strategy is designed upon an adapted data-driven Susceptible–Infected–Recovered–Deceased (SIRD) model, which includes time-varying auto-regressive immunological parameters. A novel identification procedure is proposed, composed of analytical regressions, Least-Squares optimization and auto-regressive model fits. The adapted SIRD model is validated with real data and able to adequately represent the contagion curves over large forecast horizons. The NMPC strategy is designed to generate piecewise constant quarantine guidelines, which can be reassessed (relaxed/strengthened) each week. Simulation results show that the proposed NMPC technique is able to mitigate the number of infections and progressively loosen social distancing measures. With respect to a "no-control" condition, the number of deaths could be reduced in up to 30% if the proposed NMPC coordinated health policy measures are enacted. [ABSTRACT FROM AUTHOR]

Objective: Some proposals for oxygen uptake plateau identification are based on linear regression adaptations. However, linear regression does not adequately explain the oxygen uptake nonlinear dynamics. Recently, segmented regression was considered as an alternative to fit this dynamics, by performing an approximation by straight line segments, which provided a satisfactory fit. In this context, the non-plateau and plateau hypotheses were verified by means of a Wald-type test. This work aims to extend these proposals to scenarios with autocorrelated data. Methods: We propose an algorithm to estimate the segmented regression model under autocorrelation using generalized least squares and suggest a bootstrap method to resample from the null distribution of Wald’s statistic. The performance of the estimate and methods of the plateau diagnosis were evaluated via Monte Carlo experiments. Results: The empirical results show that, under autocorrelation, the proposed estimator performs better when compared to the classic method, mainly in scenarios with small sample sizes and moderate/strong autocorrelation structure. The simulations also showed that the plateau diagnosis test has a coherent empirical Type 1 Error probability and good power. Conclusion: We proposed an alternative to estimate the parameters of a segmented regression model for autocorrelated data and an oxygen consumption plateau bootstrap test, and concluded the methods present good performance under simulated and applied case studies. Significance: The proposed method was used to model real oxygen consumption data. Empirical evidence shows that the methods can be used to objectively identify the plateau in oxygen consumption only by specifying a tolerable significance level. [ABSTRACT FROM AUTHOR]

Measurement error is common in many statistical problems and has received considerable attention in various regression contexts. In this study, we consider the random coefficient autoregressive model with measurement error possibly present in covariates. The least squares and weighted least squares methods are used to estimate the model parameters, and the consistency and asymptotic normality of the two kinds of estimators are proved. Furthermore, we propose an empirical likelihood method based on weighted score equations to construct confidence regions for the parameters. The simulation results show that the weighted least squares estimators are superior to the least squares estimators and that the confidence regions have good finite-sample behavior. At last, the model is applied to a real data example. [ABSTRACT FROM AUTHOR]

The novel circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model for non-stationary count time series is proposed. The non-stationarity of the bivariate count process is defined by a joint categorical sequence, which expresses the current state of the process. Additional cross-dependence can be generated via cross-dependent innovations. The model can also be equipped with a marginal bivariate Poisson distribution to make it suitable for low-count time series. Important stochastic properties of the new model are derived. The Yule–Walker and conditional maximum likelihood method are adopted to estimate the unknown parameters. The consistency of these estimators is established, and their finite-sample performance is investigated by a simulation study. The scope and application of the model are illustrated by a real-world data example on sales counts, where a soap product in different stores with a common circumstance factor is investigated. [ABSTRACT FROM AUTHOR]

The dynamic relationship between the value of forest carbon exchange and economic growth has a significant impact on the sustainable development of China's economy and society. Hence, the consequences cannot be ignored even when China enjoys a win-win situation concerning environment and development. This study examines the economic growth and forestry data from 1995 to 2020 (2021 data from the Statistical Yearbook of China Forestry and Grassland in February 2023 has not been released) to calculate the forest carbon sink (using the forest stock method), estimate the forest carbon sink price, and establish a panel vector autoregression model. Pulse response analysis and variance decomposition are also used to test the dynamic relationship between economic growth and forest carbon sink value. The study finding reveal that during the research period, economic growth promoted the development of forest carbon sinks; forest carbon sinks "suppressed" economic growth in the short term; and the inhibitory effect of forest carbon sinks is no longer significant. The possible innovations and contributions of this study are: 1) Expanding relevant research on the calculation of the value of forest carbon exchange, using the forest accumulation method to calculate the province's forest carbon exchange, and using the production function of the number to estimate the price of forest carbon exchange. 2) Based on the requirements of the high-quality development stage of the economy, it can serve as a reference to formulate and improve policies for relevant departments (according to the empirical results), thereby helping the country achieve the "dual carbon" goal as soon as possible. [ABSTRACT FROM AUTHOR]

An extension of the Generalized Autoregressive Score (GAS) model is presented for time series with excess null observations to include explanatory variables. An extension of the GAS model proposed by Harvey and Ito is suggested, and it is applied to precipitation data from a city in Chile. It is concluded that the model provides adequate prediction, and furthermore, an analysis of the relationship between the precipitation variable and the explanatory variables is shown. This relationship is compared with the meteorology literature, demonstrating concurrence. [ABSTRACT FROM AUTHOR]

Traditional models for areal data assume a hierarchical structure where one of the components is the random effects that spatially correlate the areas. The conditional autoregressive (CAR) model is the most popular distribution to jointly model the prior uncertainty about these spatial random effects. A limitation of the CAR distribution is its inability to accommodate high correlations between neighboring areas. We propose a new model for areal data that alleviates this problem. We represent the map by an undirected graph where the nodes are the areas, and randomly-weighted edges connect nodes that are neighbors. The model is based on a spatially-structured, multivariate Normal/Independent(NI) distribution, in which the precision matrix is indirectly built assuming a multivariate distribution for the random edge effects. The joint distribution for the edge effects is a spatial multivariate NI distribution that induces another NI distribution for the areas’ spatial effects, which inherit its capacity to accommodate outliers and heavy-tailed behavior. Most important, it can produce a higher marginal correlation between the spatial effects than the CAR model overcoming one of the main limitations of this model. We fit the proposed model to analyze real cancer maps and compared its performance with several state-of-art competitors. Our proposed model provides better fitting in almost all cases. [ABSTRACT FROM AUTHOR]

In view of the complexity and asymmetry of finite range multi-state integer-valued time series data, we propose a first-order random coefficient multinomial autoregressive model in this paper. Basic probabilistic and statistical properties of the model are discussed. Conditional least squares (CLS) and weighted conditional least squares (WCLS) estimators of the model parameters are derived, and their asymptotic properties are established. In simulation studies, we compare these two methods with the conditional maximum likelihood (CML) method to verify the proposed procedure. A real example is applied to illustrate the advantages of our model. [ABSTRACT FROM AUTHOR]

Accurately measuring systemic financial risk and analyzing its sources are important issues. This study focuses on the frequency dynamics of volatility connectedness in Chinese financial institutions using a spectral representation framework of generalized forecast error variance decomposition with the least absolute shrinkage and selection operator vector autoregression. It assesses the volatility connectedness network using complex network analysis techniques. The data are derived from 31 publicly traded Chinese financial institutions between 4 January 2011 and 31 August 2023, encompassing the Chinese stock market crash in 2015 and the COVID-19 pandemic. The frequency dynamics of the volatility connectedness results indicate that long-term connectedness peaks and cross-sectoral connectedness rises during periods of financial instability, especially in the recent bull market (2014–2015) and the 2015 Chinese stock market crash. The volatility connectedness of Chinese financial institutions declined during the COVID-19 pandemic but rose during the post-COVID-19 pandemic period. Network estimation results show that securities triggered the 2015 bull market, whereas banks were the main risk transmitters during the 2015 market crash. These results have important practical implications for supervisory authorities. [ABSTRACT FROM AUTHOR]

After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained fromthe sentiment analysis of data on Twitter posts related to the keyword "COVID-19," using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The results revealed that during COVID-19, the proposed integrated model, which trained both the Twitter sentiment values and historical VIX values, presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models. [ABSTRACT FROM AUTHOR]

Network anomaly detection solutions can analyze a network's data volume by protocol over time and can detect many kinds of cyberattacks such as exfiltration. We use exponential random graph models (ERGMs) in order to flatten hourly network topological characteristics into a time series, and Autoregressive Moving Average (ARMA) to analyze that time series and to detect potential attacks. In particular, we extend our previous method in not only demonstrating detection over hourly data but also through labeling of nodes and over the HTTP protocol. We demonstrate the effectiveness of our method using real-world data for creating exfiltration scenarios. We highlight how our method has the potential to provide a useful description of what is happening in the network structure and how this can assist cybersecurity analysts in making better decisions in conjunction with existing intrusion detection systems. Finally, we describe some strengths of our method, its accuracy based on the right selection of parameters, as well as its low computational requirements. [ABSTRACT FROM AUTHOR]

The present work proposes a new class of model for random variables with support in the positive real line, this model explains the conditional quantile and is an alternative for modeling data that indicate asymmetric behavior and heavy tails. We present a new autoregressive moving average model based on the τ-th quantile of the Burr XII distribution (BXII-ARMA) since the quantile is less sensitive than the average of heterogeneous populations and also suitable in the presence of outliers. This model makes it possible to model any quantile by a dynamic structure containing autoregressive terms and moving averages, time-varying regressors, unknown parameters, and a link function. The conditional maximum likelihood method is considered to estimate the parameters and build the confidence intervals of the BXII-ARMA model. In addition, the model is adjusted to real data related to the financial market and compared with other competing models. [ABSTRACT FROM AUTHOR]

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Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of the research on solar irradiance forecasting has been based on a single-site analysis. It is crucial to explore multisite modeling to capture variations in weather conditions between various sites, thereby producing a more robust model. In this research, we propose the use of spatial regression coupled with Gaussian Process Regression (GP Spatial) and the GP Autoregressive Spatial model (GP-AR Spatial) for the prediction of GHI using data from seven radiometric stations from South Africa and one from Namibia. The results of the proposed methods were compared with a benchmark model, the Linear Spatial Temporal Regression (LSTR) model. Five validation sets each comprised of three stations were chosen. For each validation set, the remaining five stations were used for training. Based on root mean square error, the GP model gave the most accurate forecasts across the validation sets. These results were confirmed by the statistical significance tests using the Giacommini–White test. In terms of coverage probability, there was a 100% coverage on three validation sets and the other two had 97% and 99%. The GP model dominated the other two models. One of the study's contributions is using standardized forecasts and including a nonlinear trend covariate, which improved the accuracy of the forecasts. The forecasts were combined using a monotone composite quantile regression neural network and a quantile generalized additive model. This modeling framework could be useful to power utility companies in making informed decisions when planning power grid management, including large-scale solar power integration onto the power grid. [ABSTRACT FROM AUTHOR]

We develop a Bayesian nonparametric autoregressive model applied to flexibly estimate general transition densities exhibiting nonlinear lag dependence. Our approach is related to Bayesian density regression using Dirichlet process mixtures, with the Markovian likelihood defined through the conditional distribution obtained from the mixture. This results in a Bayesian nonparametric extension of a mixtures-of-experts model formulation. We address computational challenges to posterior sampling that arise from the Markovian structure in the likelihood. The base model is illustrated with synthetic data from a classical model for population dynamics, as well as a series of waiting times between eruptions of Old Faithful Geyser. We study inferences available through the base model before extending the methodology to include automatic relevance detection among a pre-specified set of lags. Inference for global and local lag selection is explored with additional simulation studies, and the methods are illustrated through analysis of an annual time series of pink salmon abundance in a stream in Alaska. We further explore and compare transition density estimation performance for alternative configurations of the proposed model. Supplementary materials are available online. [ABSTRACT FROM AUTHOR]

Autoregressive exogenous, hereafter ARX, models are widely adopted in time series-related domains as they can be regarded as the combination of an autoregressive process and a predictive regression. Within a more complex structure, extant diagnostic checking methods face difficulties in remaining validity in many conditions existing in real applications, such as heteroscedasticity and error correlations exhibited between the ARX model itself and its exogenous processes. For these reasons, we propose a new serial correlation test method based on the profile empirical likelihood. Simulation results, as well as two real data examples, show that our method has a good performance in all mentioned conditions. [ABSTRACT FROM AUTHOR]

This study investigated the impact of energy consumption on human development in South Africa, using annual data from 1990 to 2019. The study used disaggregated data on energy measures namely: oil products consumption; electricity consumption; renewable energy consumption; natural gas; coal and lignite; and total energy consumption at an aggregate level. Human Development Index (HDI) was used as a measure of human development. By employing autoregressive distributed lag bounds test to cointegration and error correction model, the study found the impact of energy consumption on human development to be positive in the short run when renewable energy was used as a proxy, but insignificant in the long run. When oil products, natural gas and total energy were used as proxies for energy, a negative impact was confirmed in the short run, while an insignificant impact was confirmed in the long run. When electricity, coal and lignite were used as proxies for energy, an insignificant impact was confirmed, irrespective of the time frame considered. The results revealed that the positive impact of renewable energy on human development is not big enough to offset the negative impact of other energy sources. This suggests that South Africa has to continue to expand renewable energy if a positive impact of energy on human development is to be realized. [ABSTRACT FROM AUTHOR]

To analyse count time series data inflated at the r + 1 values { 0 , 1 , ... , r } , we propose a new first-order integer-valued autoregressive process with r-geometrically inflated Poisson innovations. Some statistical properties together with conditional maximum likelihood estimate are provided. For the purpose of statistical monitoring, we focus on the cumulative sum chart, exponentially weighted moving average chart and combined jumps chart towards the proposed process. Numerical simulations indicate that the conditional maximum likelihood estimator is unbiased. Moreover, the cumulative sum chart is the best choice to monitor our model in practice. Some applications about telephone complaints data are provided to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

The bootstrap procedure has emerged as a general framework to construct prediction intervals for future observations in autoregressive time series models. Such models with outlying data points are standard in real data applications, especially in the field of econometrics. These outlying data points tend to produce high forecast errors, which reduce the forecasting performances of the existing bootstrap prediction intervals calculated based on non-robust estimators. In the univariate and multivariate autoregressive time series, we propose a robust bootstrap algorithm for constructing prediction intervals and forecast regions. The proposed procedure is based on the weighted likelihood estimates and weighted residuals. Its finite sample properties are examined via a series of Monte Carlo studies and two empirical data examples. [ABSTRACT FROM AUTHOR]

High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC-VAR-EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5090 time states and 1803 locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC-VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC-VAR-EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance. [ABSTRACT FROM AUTHOR]

The present study deals with adaptive detection of radar target signal with an unknown amplitude embedded in Gaussian interference that has been modeled as an AR process. Application of such a model to the interference decreased the number of parameters to be estimated; therefore, less or even no secondary data were required to obtain a detector with the desired performance. Herein, detection was accomplished based on only the primary data. The authors resorting to the modern Kalman filtering technique developed the conventional GLRT-based detection in the presence of AR interference and proposed two new detectors: AREKF based on extended Kalman filter and ARUKF based on unscented Kalman filter. The performance assessment conducted by Monte Carlo simulation compared the proposed detectors with the existing ones based on the generalised likelihood ratio test and Kalman filter. The results revealed that the ARUKF detector could significantly outperform other detectors in terms of detection for both small number of primary datasets and high Signal-to-Noise Ratio (SNR). [ABSTRACT FROM AUTHOR]