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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]

Purpose – The purpose of this paper is to examine the relationship between financial sector development and poverty reduction in India using annual data from 1970 to 2012. The paper attempts to answer the critical question: does financial sector development lead to poverty reduction? Design/methodology/approach – Stationarity properties of the series are checked by using Ng-Perron unit root test. The paper uses the Auto Regressive Distributed Lag (ARDL) bound testing approach to co-integration to examine the existence of long-run relationship; error-correction mechanism for the short-run dynamics and Granger non-causality test to test the direction of causality. Findings – The co-integration test confirms a long-run relationship between financial development and poverty reduction for India. The ARDL test results suggest that financial development and economic growth reduces poverty in both long run and short run. The causality test confirms that there is a positive and unidirectional causality running from financial development to poverty reduction. Research limitations/implications – This study implies that poverty in India can be reduced by financial inclusion and financial accessibility to the poor. For a fast growing economy with respect to financial sector development this may have far-reaching implication toward inclusive growth. Originality/value – This paper is the first of its kind to empirically examine the causal relationship between financial sector development and poverty reduction in India using modern econometric techniques. [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]

Cold load pick-up (CLPU) has been a critical concern to utilities. Researchers and industry practitioners have underlined the impact of CLPU on distribution system design and service restoration. The recent large-scale deployment of smart meters has provided the industry with a huge amount of data that are highly granular, both temporally and spatially. In this paper, a data-driven framework is proposed for assessing CLPU demand of residential customers using smart meter data. The proposed framework consists of two interconnected layers: 1) At the feeder level, a nonlinear autoregression model is applied to estimate the diversified demand during the system restoration and calculate the CLPU demand ratio. 2) At the customer level, Gaussian mixture models and probabilistic reasoning are used to quantify the CLPU demand increase. The proposed methodology has been verified using real smart meter data and outage cases. [ABSTRACT FROM AUTHOR]

The efficacy of the multimodel framework (MMF) in modeling and identification of complex, nonlinear, and uncertain systems has been widely recognized in the literature owing to its simplicity, transparency, and mathematical tractability, allowing the use of well-known modeling analysis and control design techniques. The approach proved to be effective in addressing some of the shortcomings of other modeling techniques such as those based on a single nonlinear autoregressive network with exogenous inputs model or neural networks. A great number of researchers have contributed to this active field. Due to the significant amount of contributions and the lack of a recent survey, the review of recent developments in this field is vital. In this two-part paper, we attempt to provide a comprehensive coverage of the multimodel approach for modeling and identification of complex systems. The study contains a classification of different methods, the challenges encountered, as well as recent applications of MMF in various fields. Part 1 of this paper presents an overview of MMF for modeling and identification of nonlinear systems as well as the review of recent developments in the partitioning strategies employed. [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]

Understanding international transmission mechanism that generates the world business cycle is of immense interest. In this paper, we compile a rich global dataset and utilize a trade‐linked structural vector autoregression (SVAR) model with a relatively realistic identification scheme to construct a worldwide dynamic interdependency system. Empirical results indicate that the trade‐linked SVAR system can largely capture the common dynamic properties of national business cycle fluctuations, providing a meaningful transmission foundation to the world business cycle derived from dynamic factor models. Based on the worldwide trade‐linked SVAR system, we further shed light on three crucial topics in international economics. The findings and methods in this paper help to evaluate the macroeconomic consequences of recent trade dispute between world major economies. [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]

The graph-based block transform recently emerged as an effective tool for compressing some special signals such as depth images in 3D videos. However, in existing methods, overheads are required to describe the graph of the block, from which the decoder has to calculate the transform via time-consuming eigendecomposition. To address these problems, in this paper, we aim to develop a single graph-based transform for a class of 2D piecewise smooth signals with similar discontinuity patterns. We first consider the deterministic case with a known discontinuity location in each row. We propose a 2D first-order autoregression (2D AR1) model and a 2D graph for this type of signals. We show that the closed-form expression of the inverse of a biased Laplacian matrix of the proposed 2D graph is exactly the covariance matrix of the proposed 2D AR1 model. Therefore, the optimal transform for the signal are the eigenvectors of the proposed graph Laplacian. Next, we show that similar results hold in the random case, where the locations of the discontinuities in different rows are randomly distributed within a confined region, and we derive the closed-form expression of the corresponding optimal 2D graph Laplacian. The theory developed in this paper can be used to design both pre-computed transforms and signal-dependent transforms with low complexities. Finally, depth image coding experiments demonstrate that our methods can achieve similar performance to the state-of-the-art method, but our complexity is much lower. [ABSTRACT FROM AUTHOR]

In this paper, estimation for the generalized autoregressive conditional heteroscedasticity (GARCH) model is conducted. The Quasi likelihood (QL) and Asymptotic Quasi-likelihood (AQL) estimation methods are suggested in this paper. The QL approach relaxes the distributional assumptions of GARCH processes. The AQL technique obtains out the QL method when the conditional variance of process is unknown. The AQL methodology, merging the kernel technique used for parameter estimation of the GARCH model. This AQL methodology enables a substitute technique for parameter estimation when the conditional variance of process is unknown. Application of the QL and AQL methods to weekly prices changes of crude oil modelled by GARCH model is considered. [ABSTRACT FROM AUTHOR]

Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5 is the main particulate matter in air pollution. Therefore, how to predict PM2.5 accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5 concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5 concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5 concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5 time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5 prediction, and it is effective. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be applied to the research and development planning or sustainable technology management. The technological keywords extracted from patent documents have much information about a developed technology. We can understand the technological structure of a product by examining the results of patent analysis. So far, much research has been done on patent data analysis. The technological keywords of patent documents contain representative information on the developed technology. As such, the patent keyword is one of the most important factors in patent data analysis. In this paper, we propose a patent data analysis model combining a integer valued time series model and copula direction dependence for integer valued patent keyword analysis over time. Most patent keywords are frequency values and keywords often change over time. However, the existing patent keywords analysis works do not account for two major factors: integer value and time. For modeling integer valued keyword data with time factor, we use a copula directional dependence model based on marginal regression with a beta logit function and integer valued generalized autoregressive conditional heteroskedasticity model. Using the proposed model, we find technological trends and relations in the target technological domain. To illustrate the performance and implication of our paper, we carry out experiments using the patent documents applied and registered by Apple company. This study contributes to the effective planning for the research and development of technologies by utilizing the evolution of technology over time. [ABSTRACT FROM AUTHOR]

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application's behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems. [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]

The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results. [ABSTRACT FROM AUTHOR]

Uncertain time series analysis is a method to predict future values based on imprecisely observed values. As a basic model of uncertain time series, an uncertain autoregressive model has been presented. However, the existing paper ignores the temporal dependence information embedded in time-series data. In dealing with this issue, this paper adds a least absolute shrinkage and selection operator penalty to the traditional uncertain autoregressive model and selects the optimum order of the model according to Akaike's final prediction error criterion. Finally, two numerical examples are given to illustrate the effectiveness of the model and compare the results predicted by the uncertain autoregressive model with the principle of least squares. [ABSTRACT FROM AUTHOR]

An enhanced method to determine the best-fit auto-regressive model (AR model) for structural damage identification is proposed in this paper. Whereby, two parameters of the model, including the number of model order and the window size of data, are analyzed simultaneously in order to accomplish the optimized values by means of Akaike's Information Criterion (AIC) algorithm. The damage condition of structures can be detected by defined damage indicators obtained from the first three AR coefficients of the best-fit AR models. The ability of the proposed damage identification method is compared with the process that only utilizes conventional AR models without concern of parameter selection. The proposed method is verified using experimental data previously collected from a large-size bridge structure in the Structural Laboratory at the University of Central Florida. The results indicate that this method can detect and locate damage more effectively. [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]

This paper deals with the modeling of the first and second half number of football goals using a bivariate integer-valued first-order autoregressive model (BINAR(1)) with Negative Binomial (NB) innovations defined under timedependent moments. The main novelty of the paper is the estimation of the regression and over-dispersion parameters via a generalized quasi-likelihood (GQL) function wherein some components of the auto-covariance structure are computed through the 'working' multivariate normality assumption. The model is assessed on the Arsenal Football Club data from the period 2010 to 2016. The result of the study has revealed some important findings that illustrate the downfall of the club in the recent years as regards to the Premier League matches. [ABSTRACT FROM AUTHOR]

This paper examines the macroeconomic effects of an adverse oil price shock under different exchange rate and fiscal policy arrangements in 40 oil-exporting countries from 1973 to 2010 using panel vector autoregression techniques. The results show that output and government consumption fall in response to an oil price decline. However, the output response is significantly smaller and smoother in countries with flexible exchange rate regimes due to a larger and immediate real exchange rate depreciation. There is also less need for contractionary fiscal policy as the real depreciation appears to play a sufficient dampening role. In contrast, countries with fixed exchange rate regimes experience a small and delayed real depreciation, leaving fiscal policy to bear the bulk of the macroeconomic adjustments costs. Nevertheless, the presence of oil funds in these countries is associated with smaller fiscal spending cuts and hence a reduced output fall. These findings highlight the shock-absorbing property of flexible exchange rates and the potential macroeconomic stabilisation role of oil funds in insulating against adverse oil price movements, making a case for oil exporters to adopt more flexible exchange rate regimes and establish oil funds as fiscal buffers. [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]

This paper proposes a Bayesian semiparametric modeling approach for the return distribution in double autoregressive models. Monte Carlo investigation of finite sample properties and an empirical application are presented. The results indicate that the semiparametric model developed in this paper is valuable and competitive. [ABSTRACT FROM AUTHOR]

To improve the energy system resilience and economic efficiency, the wind power as a renewable energy starts to be deeply integrated into smart power grids. However, the wind power forecast uncertainty brings operational challenges. In order to provide a reliable guidance on operational decisions, in this paper, we propose a short-term wind power probabilistic forecast. Specifically, to model the rich dynamic behaviors of underlying physical wind power stochastic process occurring in various meteorological conditions, we first introduce an infinite Markov switching autoregressive model. This nonparametric time series model can capture the important properties in the real-world data to improve the prediction accuracy. Then, given finite historical data, the posterior distribution of flexible forecast model can correctly quantify the model estimation uncertainty. Built on it, we develop the posterior predictive distribution to rigorously quantify the overall forecasting uncertainty accounting for both inherent stochastic uncertainty and model estimation error. Therefore, the proposed approach can provide accurate and reliable short-term wind power probabilistic forecast, which can be used to support smart power grids real-time risk management. [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]

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Machining process is characterized by randomness, nonlinearity, and uncertainty, leading to the dynamic changes of machine tool machining errors. In this paper, a novel model combining the data processing merits of metabolic grey model (MGM) with that of nonlinear autoregressive (NAR) neural network is proposed for machining error prediction. The advantages and disadvantages of MGM and NAR neural network are introduced in detail, respectively. The combined model first utilizes MGM to predict the original error data and then uses NAR neural network to forecast the residual series of MGM. An experiment on the spindle machining is carried out, and a series of experimental data is used to validate the prediction performance of the combined model. The comparison of the experiment results indicates that combined model performs better than the individual model. The two-stage prediction of the combined model is characterized by high accuracy, fast speed, and robustness and can be applied to other complex machining error predictions. [ABSTRACT FROM AUTHOR]

China has been developing aggressively since its accession into the World Trade Organisation. Consequently, China has become one of the major trading partners to many countries in the world including Malaysia. To what extent China has affected Malaysian economy has been a hot issue facing the economists and practitioners. This paper examines the influence of China on Malaysian economic performances. Using structural vector autoregression (SVAR) methodology that takes into account the effect of other major trading partner countries such as the U.S., Japan and Singapore, the results indicate that different utilisation of foreign country variables to represent external sector in the model brings about different impact on domestic variables. It is shown that the U.S. is particularly important to affect domestic output while China is more important in influencing domestic inflation and the exchange rate, especially with regards to their respective income shocks. In addition, Singapore plays more dominant role in affecting domestic sector when foreign monetary policy shocks are considered. Japan is however more influential in affecting the exchange rate in some other shocks. While China is showing their dominance in the world economy, the study implies that knowing which country exactly affects which domesticvariables is very crucial in mitigating the adverse impact of foreign policy change or shocks in the process of transforming Malaysia's economy toward high income nation in the near future. [ABSTRACT FROM AUTHOR]

This paper develops a robust power system state estimation framework that accounts for correlations and imperfect time synchronization of the measurements. In this framework, correlations of the measurements obtained from the supervisory control and data acquisition (SCADA) system and the phasor measurement units (PMUs) are separately calculated through the unscented transformation and a vector auto-regression (VAR) model. Specifically, the PMU measurements during the waiting period of two successive SCADA measurement scans are buffered via a VAR model whose parameters are robustly estimated using the projection statistics. The latter take into account their temporal and spatial correlations and provide the needed measurement redundancy to suppress bad data and mitigate imperfect time synchronization. In the case where the SCADA and the PMU measurements do not arrive simultaneously at the control center, yielding imperfect measurement time synchronization, either the forecasted PMU measurements or the prior SCADA measurements from the latest state estimation run are leveraged to restore system observability. Finally, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate the measurement error correlations and to handle the outliers, also known as bad data. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework. [ABSTRACT FROM AUTHOR]

Social interactions are widely recognized to play an important role in smoking initiation among adolescents. In this paper, we hypothesize that emotionally stable, conscientious individuals are better able to resist peer pressure in the uptake of smoking. We exploit detailed friendship nominations in the US Add Health data, and extend the Spatial Autoregressive (SAR) model to deal with (i) endogenous peer selection, and (ii) unobserved contextual effects, in order to identify heterogeneity in peer effects with respect to personality. The results indicate that peer effects in the uptake of smoking are predominantly affecting individuals who are emotionally unstable. That is, emotionally unstable individuals are more vulnerable to peer pressure. This finding not only helps understanding heterogeneity in peer effects, but additionally provides a promising mechanism through which personality affects later life health and socioeconomic outcomes. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we undertake a systematic disaggregate analysis of the effects of government spending on economic activity in the US. The level of disaggregation we consider is the highest available and is unprecedented in the empirical literature. More specifically, public consumption and public investment are decomposed into various subcategories, which are measured at the levels of the federal (defence and non‐defence), and state and local governments. For each subcategory, we estimate a structural vector autoregression that identifies public spending shocks through the conditional heteroskedasticity of the structural disturbances, thus relaxing the identifying restrictions commonly used in the literature. Our analysis reveals significant heterogeneity in the effects of public spending shocks on output both across subcategories and government levels. Shocks to spending on durables and structures are found to have the largest and most persistent effect on aggregate output, with a peak multiplier that exceeds 1. Our results also suggest that there is little association between the size of a given category of public expenditures and the magnitude of its effect on output. Résumé: Séparer le bon grain de l’ivraie : une analyse désagrégée des effets des dépenses publiques aux États‐Unis. Dans ce texte, on entreprend une analyse désagrégée des effets des dépenses gouvernementales sur l’activité économique aux États‐Unis. Le niveau de désagrégation considéré est le plus grand possible et est sans précédent dans la littérature empirique. Plus spécifiquement, la consommation et l’investissement publics sont décomposés en diverses sous‐catégories qui sont mesurées pour le gouvernement fédéral (défense et non‐défense), ainsi que pour les gouvernements des états et locaux. Pour chaque sous‐catégorie, on estime un vecteur autorégressif structurel qui identifie les chocs de dépenses publiques à travers l’hétéroscédastictité conditionnelle des perturbations structurelles, pour ainsi relâcher les restrictions d’identification communément utilisées dans la littérature. L’analyse révèle une hétérogénéité significative dans les effets des chocs de dépenses publiques sur la production agrégée selon les sous‐catégories et les niveaux de gouvernement. Les chocs dans les dépenses sur les biens durables et les structures tendent à avoir les effets les plus grands et les plus persistants sur la production agrégée avec un effet multiplicateur qui excède 1. Les résultats suggèrent qu’il y a peu de rapport entre la taille d’une catégorie donnée dans les dépenses publiques et l’ampleur de l’effet sur la production agrégée. [ABSTRACT FROM AUTHOR]

This paper applies annual data from 1962 to 2011 to investigate the long run relationship between government spending and Gross Domestic Product (GDP). The common approach only considers defense government spending to estimate the multiplier to overcome the identification problem and endogeneity in isolating the effect of changes in government spending on GDP, I use the Autoregressive Distributed Lag (ARDL) approach to cointegration, which works despite having endogenous regressors to estimate the spending multiplier. The results confirm that government spending can be treated as a 'long-run forcing' variable for the explanation of real GDP and the long-run multiplier is found to be 1.94. [ABSTRACT FROM AUTHOR]

Data analytics is played a vital role in Information Technology and Information Technology essential services ITeS for making effective decisions. The demand for tourism and prediction of tourist arrivals are important for tourism organisation. In this paper, we analyse tourist and extract information using data analytics process. The web data are processed using travellers' details and applying an aggregate function to calculate the searching index. Here, we use the autoregression analytics method for accurate prediction. The tourist information is recorded and creates a system log for processing and extracting information. The interaction between each record and their logs are used for data processing and analytics model. This paper uses a recommendation system for the data analytics process and compares the results with existing models. Our proposed method provides good and accurate results for tourism organisation. [ABSTRACT FROM AUTHOR]

This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1) and I(2) models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as the ability to find the overall maximum. The next step is to compare their efficiency and reliability across experiments. The aim of the paper is to commence a collective learning project by the profession on the actual properties of algorithms for cointegrated vector autoregressive model estimation, in order to improve their quality and, as a consequence, also the reliability of empirical research. [ABSTRACT FROM AUTHOR]

Purpose India is a developing nation where the marginal benefit of infrastructure development is tremendous. The purpose of this paper is to analyze the relationship between infrastructure development and poverty reduction for India using the yearly data from 1991 to 2015.Design/methodology/approach The authors use the principal component analysis to construct indices for four major sub-sectors, namely, transport, water and sanitation, telecommunications and energy, falling under the broad infrastructure sector and then using these sectorwise indices, the authors construct an overall index which represents infrastructure development. The authors provide evidence on the link between infrastructure development and poverty reduction by using the auto regressive distributed lag (ARDL) bound testing approach.Findings The ARDL test results suggest that infrastructure development and economic growth reduce poverty in both long run and short run. The causality test confirms that there is a positive and unidirectional causality running from infrastructure development to poverty reduction.Research limitations/implications The study confirms that India’s Infrastructure development plays a vital role in reducing poverty and calls for the Indian Government to adopt economic policies which are aimed at developing and strengthening the infrastructure levels and bringing in more investment in the infrastructure sector in order to help the poor population by making them exposed to better opportunities of employment and income growth, thereby achieving the goal of poverty reduction.Originality/value This paper is a fresh and unique attempt of its kind to empirically investigate the causal relationship between infrastructure development and poverty reduction in India using modern econometric techniques. [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]

In this paper, we propose an extreme conditional quantile estimator. Derivation of the estimator is based on extreme quantile autoregression. A noncrossing restriction is added during estimation to avert possible quantile crossing. Consistency of the estimator is derived, and simulation results to support its validity are also presented. Using Average Root Mean Squared Error (ARMSE), we compare the performance of our estimator with the performances of two existing extreme conditional quantile estimators. Backtest results of the one-day-ahead conditional Value at Risk forecasts are also given. [ABSTRACT FROM AUTHOR]

This paper proposed a novel modeling method using the running process data, i.e., the reference input positions and the actual output positions, based on the Naïve Bayes method and a nonlinear autoregressive long-short term memory network (i.e., the NAR-LSTM) to address the nonlinear time-series modeling problem for increasing the prediction accuracy of the model of CNC machine feed drive system. A Naïve Bayes based motion states classifier (i.e., the NB-MSC) is proposed to automatically classify the motion states with knowledge of dynamic characteristics of the feed drive system for constructing the submodels of different motion states (startup state, reverse state, etc.). In addition, a model based multi-objective optimization method is presented to extract samples for the training of the NB-MSC. Then by modifying the basic long-short term memory (LSTM) network, the NAR-LSTM network is proposed to construct those submodels. Compared to the existing modeling methods via dynamic analysis, the proposed method is better because it can achieve higher prediction accuracy on highly nonlinear motion states such as the reverse state. To validate the proposed methods, a set of experiments are conducted to prove the feasibility of the feed drive model as well as the advantages of the NB-MSC and the NAR-LSTM network in improving the modeling performance. [ABSTRACT FROM AUTHOR]

The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models' performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. [ABSTRACT FROM AUTHOR]

It is challenging to track a target continuously in videos with long-term occlusion, or objects which leave then re-enter a scene. Existing tracking algorithms combined with onlinetrained object detectors perform unreliably in complex conditions, and can only provide discontinuous trajectories with jumps in position when the object is occluded. This paper proposes a novel framework of tracking-by-detection using selection and completion to solve the abovementioned problems. It has two components, tracking and trajectory completion. An offline-trained object detector can localize objects in the same category as the object being tracked. The object detector is based on a highly accurate deep learning model. The object selector determines which object should be used to re-initialize a traditional tracker. As the object selector is trained online, it allows the framework to be adaptable. During completion, a predictive non-linear autoregressive neural network completes any discontinuous trajectory. The tracking component is an online real-time algorithm, and the completion part is an after-theevent mechanism. Quantitative experiments show a significant improvement in robustness over prior state-of- the-art methods. [ABSTRACT FROM AUTHOR]

This paper evaluates the effectiveness of fiscal policy by employing a structural panel vector autoregression model with a shock to fiscal spending identified via theoretical robust sign restrictions. Using an annual data set of 120 countries over the period 1960--2014, the results show that fiscal multipliers are larger in advanced economies, when public debt is low, at a high level of financial development, in a financial crisis and during business cycle downturns. Contrary to conventional wisdom, fiscal multipliers are not necessarily smaller in countries that are relatively open to trade and capital flows and operating under flexible exchange rate regimes. The relationship between the size of fiscal multipliers and the three dimensions of openness--trade openness, capital mobility and exchange rate flexibility--hinges on the response of the real exchange rate and the domestic monetary policy pursued. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to investigate the income inequality dynamics in each of the 50 states of USA over the period 1981-2011.Design/methodology/approach The paper estimates an augmented Kuznets curve panel Vector AutoRegression in per capita income, economic freedom, educational attainment, unemployment, and population ageing along with evaluating generalized impulse responses functions (GIRF) and generalized forecast-error variance decompositions (GFEVD).Findings All the variables are integrated of order one and are panel cointegrated. Kuznets’ hypothesized inverted U-shaped relationship between inequality and growth is not supported by the data. Unemployment and population ageing have statistically significant positive effects on inequality in the long-run; education has statistically significant negative impact; economic freedom has statistically insignificant positive effect. Long-run bidirectional causality exists among the variables. GFEVD show that excluding income inequality itself, variation in income inequality is more influenced by perturbations in per capita income, educational attainment, and unemployment. GIRF corroborate the results of the GFEVD.Originality/value This paper fulfills an identified need to study the causal relationship between inequality and its determining factors without assuming the a priori exogeneity or endogeneity of the underlying variables. [ABSTRACT FROM AUTHOR]