Autoregressive Component Research Articles

Understanding the transmission dynamics of a large-scale measles outbreak in Southern Vietnam

ObjectivesSouthern Vietnam experienced a large measles outbreak of over 26,000 cases during 2018-2020. We aimed to understand and quantify the measles spread in space-time dependence and the transmissibility during the outbreak. MethodsMeasles surveillance reported cases between January 2018 and June 2020, vaccination coverage, and population data at provinicial level were used. To illustrate the spatio-temporal pattern of disease spread, we employed the endemic-epidemic multivariate time series model decomposing measles risk additively into autoregressive, spatio-temporal, and endemic components. Likelihood-based estimation procedures were performed to determine the time-varying reproductive number Re of measles. ResultsOur analysis showed that the incidence of measles was associated with vaccination coverage heterogeneity and spatial interaction between provincial units. The risk of infections was dominated by between-province transmission (36.1% to 78.8%), followed by local endogenous transmission (4.1% to 61.5%). In contrast, the endemic behavior had a relatively small contribution (2.4% to 33.4%) across provinces. In the exponential phase of the epidemic, Re was above the threshold with a maximum value of 2.34 (95% CI: 2.20-2.46). ConclusionLocal vaccination coverage and human mobility are important factors contributing to the measles dynamics in Southern Vietnam, and the high risk of inter-provincial transmission is of most concern. Strengthening the disease surveillance is recommended, and further research is essential to understand the relative contribution of population immunity and control measures in measles epidemics.

Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle

This paper tests the random walk hypothesis in the cryptocurrency market. Based on the well-known Meese–Rogoff puzzle, we evaluate whether cryptocurrency returns are predictable or not. For this purpose, we conduct in-sample and out-of-sample analyses to examine the forecasting power of our model built with autoregressive components and lagged returns of BITCOIN, compared with the random walk benchmark. To this end, we considered the 13 major cryptocurrencies between 2018 and 2022. Our results indicate that our models significantly outperform the random walk benchmark. In particular, cryptocurrencies tend to be far more persistent than regular exchange rates, and BITCOIN (BTC) seems to improve the predictive accuracy of our models for some cryptocurrencies. Furthermore, while the predictive performance is time varying, we find predictive ability in different regimes before and during the pandemic crisis. We think that these results are helpful to policymakers and investors because they open a new perspective on cryptocurrency investing strategies and regulations to improve financial stability.

Tourist arrivals and overnight stays along the Croatian Adriatic Coast: Changes in persistence and seasonality from the COVID-19 disruption

This study examines the changes in the persistence and seasonality inherent in the Croatian tourism sector in light of the onset of the COVID-19 pandemic. First, we differentiate between the changes in the persistence and seasonal behavior with respect to domestic and foreign tourist arrivals and overnight stays. Second, with nearly 90% of the Croatian tourism sector tied to the seven counties along the Adriatic coast we investigate the differential regional impact on persistence and seasonal behavior. Our results indicate the disruption was much more prominent for foreign tourist arrivals and overnight stays relative to domestic tourist arrivals and overnight stays with respect to the increased persistence associated with the onset of the pandemic along with the seasonal autoregressive component reduced considerably.

Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events

A Bayesian dynamic linear model (BDLM) framework for data modeling and forecasting is proposed to evaluate the performance of an operational cable-stayed bridge, that is, Ting Kau Bridge in Hong Kong, by using SHM strain field data acquired. One of the major challenges in dealing with the existing in-service bridge under extreme typhoon loads is to forecast structural behavior using the typhoon response exhibiting non-stationarity, large data fluctuations and strong randomness. The first attempt for SHM data modeling during extreme events, that is, typhoons, using BDLM framework, was conducted in this study. The data from multiple sensors are analyzed for one-step, multi-step ahead forecasting and missing data imputation. The overall bridge behavior is incorporated into a forecasting model by superposition of forecasting results of trend (representing the structural baseline response), periodic component (response component evolving regularly over time), and autoregressive component (time-dependent error) through BDLM algorithm. The results demonstrate that the BDLM framework yielded more accurate calculations compared with Gaussian process and Variational Heteroscedasticity Gaussian Process methods with respect to one-step ahead forecasting for strain data under typhoons. Multi-step ahead forecasting was successfully carried out both for non-typhoon and typhoon responses within an acceptable precision range. The correlation between periodic component and temperature was also investigated. Regarding missing data imputation, BLDM algorithm can generate robust results due to making full use of the monitoring data both before and after the missing segments.

Spatiotemporal Modelling of Endemic-epidemic Cholera in Nigeria

This study used a multivariate negative binomial model to capture the Spatiotemporal endemic-epidemic of infectious disease and explore the spatial and temporal patterns of cholera outbreaks in Nigeria. The model for the epidemic part measured spatial weights for the disease spread across the geographical neighboring regions and the endemic part accounted for temporal variation of disease incidence. Weekly count data on cholera from the Nigeria Department of Disease Control and Monitoring Epidemiology (NCDC SED) between January 1st and November 19th, 2018 was used to illustrate the model. In fitting the model, the study has shown that the model with seasonality and autoregressive components provided an adequate fit for the cholera count data and also perform better than the model without seasonality and autoregression for modelling the Spatiotemporal dependency structure of cholera disease

A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy

The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.

Алгоритм методу ідентифікації моделі авторегресії-ковзного середнього, який узагальнює методику Юла-Уокера, та його програмна Python-реалізація

The paper presents a detailed 11-step algorithm for practical implementation of a new identification method of autoregressive–moving-average model ARMA(nap, nkc)  of prediction of stationary time series with arbitrary values of the orders nap, nkc . The new method, published in the previous authors' works, is a generalization of the well-known Yule–Walker method. The algorithm is implemented under the condition, proven by the authors in the previous publications, that the optimal structure of the ARMA(nap, nkc) model is the ARMA(3,3). A feature of this algorithm is that the parameters of the autoregressive component of the ARMA(3,3) model are determined using the fourth, fifth, and sixth autocovariances, which significantly distinguishes it from the traditional algorithm for identifying this class of models using the Yule–Walker method, which uses only autocovariances of the first, second, and third orders. Another feature of the algorithm is a straightforward procedure of determining the parameters of the moving average that does not require renewing the minimizing the residual sum of squares procedure when moving to other orders of the autoregressive and the moving average components, unlike the traditional approaches. The article presents a Python program implementation of the proposed identification algorithm and a demonstration of its effectiveness in solving the problem of identifying the ARMA(3,3) model for a specific time series given by an experimental implementation. The paper also determines the conditions for the experimental implementation of the time series to provide more accurate forecasting compared to the traditional approach.

Multitemporal analysis with statistical models: influence of the atmospheric condition on urban concentrations of particulate matter

This paper shows a multitemporal analysis with autoregressive integrated moving average models of the influence of atmospheric condition on concentrations of particulate matter ≤ 10 µm in Bogotá city, Colombia. Information was collected from six monitoring stations distributed throughout the city. The study period was nine years. Autoregressive component of the models suggests that urban areas with greater atmospheric instability show a lower hourly persistence of particulate matter (one hour) compared to urban areas with lower atmospheric instability (two hours). Moving average component of the models hints those urban areas with greater atmospheric instability show greater hourly variability in particulate matter concentrations (5-10 hours). The models also suggest that a high degree of air pollution decreases the temporal influence of the atmospheric condition on particulate matter concentrations; in this case, the temporal behavior of particulate matter possibly depends on the urban emission sources of this pollutant rather than on the existing atmospheric condition. This study is relevant to deepen the knowledge in relation to the following aspects of atmospheric physics: The use of statistical models for the time series analysis of atmospheric condition, and the analysis by statistical models of the influence of atmospheric condition on air pollutant concentrations.

A Novel Model Based on DA-RNN Network and Skip Gated Recurrent Neural Network for Periodic Time Series Forecasting

Deep learning models are playing an increasingly important role in time series forecasting with their excellent predictive ability and the convenience of not requiring complex feature engineering. However, the existing deep learning models still have shortcomings in dealing with periodic and long-distance dependent sequences, which lead to unsatisfactory forecasting performance on this type of dataset. To handle these two issues better, this paper proposes a novel periodic time series forecasting model based on DA-RNN, called DA-SKIP. Using the idea of task decomposition, the novel model, based on DA-RNN, GRU-SKIP and autoregressive component, breaks down the prediction of periodic time series into three parts: linear forecasting, nonlinear forecasting and periodic forecasting. The results of the experiments on Solar Energy, Electricity Consumption and Air Quality datasets show that the proposed model outperforms the three comparison models in capturing periodicity and long-distance dependence features of sequences.

A Methodological Note: An Introduction to Autoregressive Models.

The autoregressive model is a useful tool to analyze longitudinal data. It is particularly suitable for gerontological research as autoregressive models can be used to establish the causal relationship within a single variable over time as well as the causal ordering between two or more variables (e.g., physical health and psychological well-being) over time through bivariate autoregressive cross-lagged or contemporaneous models. Specifically, bivariate autoregressive models can explore the cross-lagged effects between two variables over time to determine the proper causal ordering between these variables. The advantage of analyzing cross-lagged effects is to test for the strength of prediction between two variables controlling for each variable's previous time score as well as the autoregressive component of the model. Bivariate autoregressive contemporaneous models can also be used to determine causal ordering within the same time point when compared to cross-lagged effects. Since the technique uses structural equation modeling, models are also adjusted for measurement error. This paper will present an introduction to setting up models and a step-by-step approach to analyzing univariate simplex autoregressive models, bivariate autoregressive cross-lagged models, and bivariate autoregressive contemporaneous models.

Liquidity fluctuations and the latent dynamics of price impact

Market liquidity is a latent and dynamic variable. We propose a dynamical linear price impact model at high frequency in which the price impact coefficient is a product of a daily, a diurnal, and an auto-regressive stochastic intraday component. We estimate the model using a Kalman filter on order book data for stocks traded on the NASDAQ in 2016. We show that our price changes estimates, conditional on order flow imbalance, on average of real price changes variance. Evidence is also provided on the fact that the conditioning on filtered information improves the estimate of the LOB liquidity with respect to the one obtained from a static estimation of the price impact. In addition, an out-of-sample analysis shows that our model provides a superior out-of-sample forecast of price impact with respect to historical estimates.

Spatio-temporal Object-Oriented Bayesian Network modelling of the COVID-19 Italian outbreak data

The spatial epidemic dynamics of COVID-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Following an autoregressive approach, both spatial and time components have been embedded in the model by means of spatial and time lagged variables. The model could be a valid instrument to support or validate policy makers’ decisions strategies.

Time series model for real-time forecasting of Australian photovoltaic solar farms power output

Intermittent electrical power output from grid-connected solar farms causes intermittent and uncertain requirements for dispatchable power to balance power supply and demand. Accurate forecasting of electrical power output from solar farms can improve managing power generators connected to the grid. To forecast the electrical power output, a time series model is developed for two solar farms in Australia. The forecast model consists of a Fourier series that models seasonality and an autoregressive moving-average component that models the difference between the observed electrical power outputs and the Fourier series. Persistence detection is added to the model to improve forecast performance on clear days. Using minutely data, the model forecasts the electrical power output seven minutes ahead at every five-minute interval to comply with the requirements of the Australian Energy Market Operator (AEMO). Based on a 30-day testing period, the normalized mean absolute error (NMAE) skills of the time series model are 10.9% and 13.2% lower than those of the clear sky index persistence (CSIP) model. However, the normalized root mean squared error (NRMSE) skills of the time series model are approximately 3% and 12% higher than those of CSIP and the model currently used by AEMO, respectively. As the NRMSE skills are more indicative than the NMAE skills in reducing large forecast errors that would reduce electricity grid stability, the results suggest that AEMO can improve the management of the electricity grid with an inexpensive tool by adopting the developed model to forecast electrical power output of solar farms.

A MIXED BILINEAR INAR(1) MODEL

The paper introduces a new autoregressive model of order one for time seriesof counts. The model is comprised of a linear as well as bilinear autoregressive component. These two components are governed by random coefficients. The autoregression is achieved by using the negative binomial thinning operator. The method of moments and the conditional maximum likelihood method are discussed for the parameter estimation. The practicality of the model is presented on a real data set.

Forecasting mortality with international linkages: A global vector-autoregression approach

This paper proposes a Global Vector Autoregression (GVAR) mortality model to simultaneously model and forecast multi-population mortality dynamics. The proposed GVAR model decomposes the global regression model into population-wise local systems. Each local system consists of an intra-population autoregressive component and a small set of global factors, which contain systematic mortality information of all populations. Such a decomposition substantially reduces the extra estimation cost of including new populations compared to unconstrained VAR models, and makes the GVAR model an efficient tool for analyzing the joint mortality dynamics of a large group of populations. Further, under fairly general assumptions, the proposed GVAR model could generate coherent mortality projections between any two ages in any two populations. Using single-age mortality data of 15 low-mortality countries, we find that the global factors have substantial explanatory and forecasting power of mortality changes of individual populations, and the proposed GVAR model could produce satisfying mortality forecasts under various settings.

Estimation of time-varying autoregressive stochastic volatility models with stable innovations

A new time-varying autoregressive stochastic volatility model with $$\alpha $$ -stable innovations (TVAR $$\alpha $$ SV) is proposed. This new model for time series data combines a time-varying autoregressive component and a stochastic scaling as known from stochastic volatility models with $$\alpha $$ -stable distributed noise. Hence, the model can cover extreme events better than classical stochastic volatility models. Furthermore, we develop a Gibbs sampling procedure for the estimation of the model parameters. The procedure is based on the estimation strategy by Kim et al. (Rev Econ Stud 65(3): 361–393, 1998) for classical stochastic volatility models, however, the estimation procedure requires a deliberate approximation of $$\alpha $$ -stable distributions by finite mixtures of normal distributions and the application of a simulation smoother for linear Gaussian state space models. A simulation study for the new estimation procedure illustrates the appealing accuracy. Finally, we apply the model to electricity spot price data.

Where did the time (series) go? Estimation of marginal emission factors with autoregressive components

This paper offers a novel contribution to the literature on marginal emission factors by proposing a robust empirical methodology for their estimation across both time and space. Our ARIMA model with time-effects not only outperforms the established models in the economics literature but it also proves more reliable than variations adopted in the field of engineering. Utilising half-hourly data on carbon emissions and generation in Great Britain, the results allow us to identify a more stable path of MEFs than obtained with existing methodologies. We also estimate marginal emission effects over subsequent time periods (intra-day), rather than focussing only on individual settlement periods (inter-day). This allows us to evaluate the annual cycle of emissions as a result of changes in the economic and social activity which drives demand. Moreover, the reliability of our approach is further confirmed upon exploring the cross-country context. Indeed, our methodology proves reliable when applied to the case of Italy, which is characterised by a different data generation process. Crucially, we provide a more robust basis for valuing actual carbon emission reductions, especially in electricity systems with high penetration of intermittent renewable technologies.

Mixed Geographically and Temporally Weighted Autoregressive to Modeling the Levels of Poverty Population in Java in 2012-2018

Geographically and temporally weighted regression (GTWR) is a method applied when there is spatial and temporal diversity in the observation. In the GTWR model not all variables were local or insignificant (global in nature) so the GTWR model was developed into Mixed Geographically and Temporally Weighted Regression (MGTWR). The GTWR model can also add an autoregressive component of response variable, the resulting model is known as a geographically and temporally weighted autoregressive model (GTWAR). This study develops the MGTWR model and the GTWAR model which produces a Geographically and Temporally Weighted Autoregressive model (MGTWAR) which is used to model data on the percentage of poor people in regency/municipality on Java Island in 2012-2018. The results showed that MGTWAR produced Akaike Information Criterion (AIC) smaller than GTWR, and the coefficient of determination (R2) is higher than GTWR.

Within-individual relations of emotional, social, and work self-efficacy beliefs to organizational citizenship behaviors

Organizational citizenship behaviors (OCBs) represent a specific class of prosocial behaviors observable in the organizational environment. This study examined the longitudinal relations among regulatory emotional, social and work self-efficacy beliefs, and their relations to Organizational Citizenship Behaviors directed at specific individuals (OCBIs) at work, using a Latent State-Trait Model with Autoregressive effects (LST-A). The LST-A decomposes variance into trait, autoregressive, and occasion-related components, and allowed for testing of all hypotheses at the within-worker level. The model was tested using longitudinal data from N = 562 italian workers. Results supported the hypotheses that both social and work self-efficacy fully mediated the longitudinal relations between regulatory emotional self-efficacy in managing negative emotions at work and OCBIs. Theoretical and practical contributions of the findings are discussed.

Spatio-temporal modelling of [formula omitted] daily concentrations in Italy using the SPDE approach

This paper illustrates the main results of a spatio-temporal interpolation process of PM10 concentrations at daily resolution using a set of 410 monitoring sites, distributed throughout the Italian territory, for the year 2015. The interpolation process is based on a Bayesian hierarchical model where the spatial-component is represented through the Stochastic Partial Differential Equation (SPDE) approach with a lag-1 temporal autoregressive component (AR1). Inference is performed through the Integrated Nested Laplace Approximation (INLA). Our model includes 11 spatial and spatio-temporal predictors, including meteorological variables and Aerosol Optical Depth. As the predictors’ impact varies across months, the regression is based on 12 monthly models with the same set of covariates. The predictive model performance has been analyzed using a cross-validation study. Our results show that the predicted and the observed values are well in accordance (correlation range: 0.79–0.91; bias: 0.22–1.07μg/m3; RMSE: 4.9–13.9μg/m3). The model final output is a set of 365 gridded (1 km × 1 km) daily PM10 maps over Italy equipped with an uncertainty measure. The spatial prediction performance shows that the interpolation procedure is able to reproduce the large scale data features without unrealistic artifacts in the generated PM10 surfaces. The paper presents also two illustrative examples of practical applications of our model, exceedance probability and population exposure maps.