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Mobility is considered one of the main reasons for the COVID-19 spread. Predicting the effect of control measures on mobility is essential to apply effective decisions. This work proposes an AI-based model for mobility changes prediction. The proposed CNN-LSTM with Autoregression has achieved the best results compared to other investigated models. Results show that the proposed model can predict the effect of precaution control measures on future community mobility with minimum loss. The mean absolute error over all countries in the study is 5.3 . For Egypt and Saudi Arabia, the model achieved an MAE loss of 4.6 and 3.7 consecutively.

Using a quantile vector autoregressive model to capture return dynamics in extreme market conditions, we find that the cryptocurrency market exhibits a high level of market connectedness. Bitcoin is a net transmitter of return spillovers during busts and a net receiver during booms. Analysis of the timing of bubble and crash periods uncovers the presence of interdependence and contagion effects. Asset dynamics is driven to a great extent by the technology, in particular the consensus protocol of cryptocurrencies. There is only limited evidence for asset rotation, and it involves mostly Ripple.

Robust modeling of a multimodal dynamic system is a challenging and fast-growing area of research. In this study, an integrated bi-modal computing paradigm based on Nonlinear Autoregressive Radial Basis Functions (NAR-RBFs) neural network model, a new family of deep learning with the strength of hybrid artificial neural network, is presented for the solution of nonlinear chaotic dusty system (NCDS) of tiny ionized gas particles arising in fusion devices, industry, astronomy, and space. In the proposed methodology, special transformations are introduced for a class of differential equations, which convert the local optimum to a global optimum. The proposed NAR-RBFs neural network model is implemented on bi-model NCDS represented with Van der Pol-Methiew Equation (VdP-ME) for different scenarios based on variation in dust gain production and loss for both small and large time domains. Excellent agreement for proposed bimodal computing paradigm by the result with the standard state of the arts numerical solvers is verified by attaining RMSE up to 1E−38 for the nonlinear VDP-ME. Accuracy of the proposed model in the critical time domain is also validated by convergence, stability and consistency analysis on statistics calculated from absolute error, root-mean-square error, and analysis of variance metrics.

Multivariate count time series models are an important tool for the analysis and prediction of infectious disease spread. We consider the endemic-epidemic framework, an autoregressive model class for infectious disease surveillance counts, and replace the default autoregression on counts from the previous time period with more flexible weighting schemes inspired by discrete-time serial interval distributions. We employ three different parametric formulations, each with an additional unknown weighting parameter estimated via a profile likelihood approach, and compare them to an unrestricted nonparametric approach. The new methods are illustrated in a univariate analysis of dengue fever incidence in San Juan, Puerto Rico, and a spatio-temporal study of viral gastroenteritis in the twelve districts of Berlin. We assess the predictive performance of the suggested models and several reference models at various forecast horizons. In both applications, the performance of the endemic-epidemic models is considerably improved by the proposed weighting schemes., Comment: A previous version of this paper had the title "Multivariate endemic-epidemic models with higher-order lags and an application to outbreak detection" (see v1)

• Extra-terrestrial Solar Irradiance has been validated for PV output forecasting. • The machine learning approach successfully captures huge intra-daily PV output variations. • Study paves the way to develop a simple and effective PV output forecasting approach. A B S T R A C T The inherently intermittent nature of solar irradiance and other meteorological variables means that accurate forecasting of the photovoltaic power output is essential for planning and balancing photovoltaic power systems. This study proposes a novel approach to predicting one-week-ahead half-hourly photovoltaic power output in the United Kingdom using sloped extra-terrestrial irradiance and weather data (e.g., cloud-cover and temperature) as input parameters. A Non-linear Autoregressive Exogenous Neural Network is trained on a three-year historical dataset from two photovoltaic plants in the United Kingdom with capacities of 53 and 103 MWp. The forecasting model captures huge intra-daily variations of photovoltaic output, which is particularly useful to balance the supply and demand of the electricity system. The result of the study validates the concept of using sloped extra-terrestrial irradiance as an input parameter and suggests that meteorological conditions will dictate the accuracy of predictions. Findings also indicate that the use of sloped extra-terrestrial irradiance in conjunction with cloud-cover presented the optimal combination of input parameters as these provided the simplest and most cost-effective model without reducing accuracy. The approach can have universal value as it only requires coordinates and weather data. There is now a strong imperative to use the model in other locations where the weather is more stable.

Digital camera monitoring has revolutionised survey designs in many fields, as an important source of information. The extended sampling coverage offered by this monitoring scheme makes it preferable compared to other traditional methods of survey. However, data obtained from digital camera monitoring are often highly variable, and characterized by sparse periods of zero counts, interspersed with missing observations due to outages. In practice, missing data of relatively shorter duration are mostly observed and are often imputed using interpolation techniques, ignoring long-term trends leading to inherent estimation biases. In this study, we investigated time series forecasting methods that adequately handle intermittency and produced plausible estimates for imputation and forecasting purposes. The study utilised a yearlong digital camera monitoring data set of hourly counts of powerboat launches at three boat ramps in Western Australia. Several time series forecasting methods were evaluated and the accuracies of their point estimates of forecasts for various lead times in hours of up to one week were assessed using cross-validation techniques. Intermittent demand forecasting techniques, including Croston's method and Syntetos-Boylan Approximation (SBA) models, and count data forecasting methods including autoregressive conditional Poisson (ACP) models, integer-valued moving average (INMA) models, and integer-valued autoregressive (INAR) models were evaluated. ACP and INAR models performed better than intermittent demand forecasting techniques for short forecast horizons and provided some evidence of their sufficiency in predicting the dynamics in recreational boating activities. This result established that, in as much as intermittency may be a key feature for a given dataset, it should not override the systemic characteristics of data in the application of forecasting techniques. Our results provide plausible estimates for short-term missing data and forecasts for monitoring events, with applications in supporting proper tracking of usage of facilities, guiding resource allocations and providing insightful perspectives for management decisions., Digital camera monitoring, Time series modelling, Intermittent data, Integer-valued models, Imputation, Forecasting.

This paper compares alternative univariate versus multivariate models, frequentist versus Bayesian autoregressive and vector autoregressive specifications, for hourly day-ahead electricity prices, both with and without renewable energy sources. The accuracy of point and density forecasts are inspected in four main European markets (Germany, Denmark, Italy and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian VAR specifications with exogenous variables dominate other multivariate and univariate specifications, in terms of both point and density forecasting., Forthcoming in "International Journal of Forecasting"

Our study focusses on establishing portfolio-based momentum profits in the Indian market, and on designing a model to identify portfolio-specific and macroeconomic factors generating abnormal returns. We empirically examine returns of long-term and short-term winners and losers’ portfolios to establish the existence of extra-normal profits similar to those documented by Jegadeesh and Titman (1993). Using vector autoregressive methodology, we find price–earnings ratio, price–book ratio, and net foreign institutional inflows as significant factors in momentum generation. We further decompose momentum profits to test for time-series, cross-sectional and lead-lag components. Our study provides insights to portfolio managers in exploring the concept of momentum during portfolio designing.

Research community has a growing interest in neural networks because of their practical applications in many fields for accurate modeling and prediction of the complex behavior of systems arising from engineering, economics, business, financial and metrological fields. Artificial neural networks (ANN) are very flexible function approximations tool used as universal modeling based on the separating of the past dynamics into clusters, in which we construct local models to capture the potential growth of the series depends on the previously known values. In this study, rain data of five major cities of Sindh province of Pakistan is considered, and summer rainfall of these five synoptic stations are statistically evaluated for prediction. The nonlinear autoregressive network with exogenous inputs (NARX) model for a time series is analyzed to evaluate the pattern of precipitation. We train a highly nonlinear NARX network model from randomly generated initial weights that converged to the best solution with the help of the Levenberg-Marquardt algorithm. A multi-step ahead NARX response time predictor is developed for rain forecasting. The performance of the NARX model is viable to capture nonlinear behavior with a high value of correlation coefficient R ranging from 0.70 to 0.99 for different synoptic stations. The results calculated using the proposed NARX neural network time series approach are accurate and reliable based on the coefficient of correlation and mean square error indices for rainfall forecasting. Keywords: Artificial neural network, NARX model, Levenberg-Marquardt algorithm, Summer rainfall

In this article our goal is to studying the stationary condition of EGARCH time series model which is proved by Nelson (1991) but what we did is to prove it with more simple and clearly defined method and this is done by using a dynamical approximation relying on local linearization method in the neighborhood of a non-zero singular point of EGARCH model. This method is being used to approximate a Nonlinear model to a linear autoregressive model. Beside that we found the orbitally stability condition of a limit cycle of EGARCH(1,1) model if the model possess it. Keywords: EGARCH, Local linearization method, Conditional variance, Limit cycle, Time series, Autoregressive model

We have been developing a measurement system for microstructures using a small-diameter optical fiber probe. The contact detection method using a threshold value may cause a large error due to the time lag between the real contact time and the time detected by the measurement system. In this paper, we investigated a method for detecting the starting point of contact using an AR model and LSTM, a type of deep learning when the stylus tip contacts the measured surface. The results of measurement experiments using an inclined surface showed that the detection accuracy using AR model decreased as the inclination angle increased, but the LSTM was able to detect the contact point with high accuracy even when the inclination angle increased. It was also confirmed that the method using LSTM can detect contact with high accuracy without being affected by drift over time and various noises (other than random noise).

This paper investigates the impact of uncertainty shocks on REITs returns over a monthly period from 1972:01 to 2015:12, and sub-samples from 1972:01 to 2009:06, and 2009:07 to 2015:12, to accommodate for the possible effects of the Global Financial Crisis (GFC) and unconventional monetary policy decisions. We use the recently-proposed variations in the price of gold, around events associated with unexpected changes in uncertainty as an instrument to identify uncertainty shocks in a proxy Structural Vector Autoregressive (SVAR) model. Moreover, to control for news-related effects associated with these events, uncertainty and news shocks are jointly identified based on a set-identified proxy SVAR, as recently suggested in the VAR literature. Our results show that the uncertainty shock generates a larger negative impact on REITs returns over the post-GFC period to the extent that it also outweighs the impact of the otherwise dominant news (productivity) shocks. In addition, the impulse response dynamics related to the recursively identified uncertainty shock, as is standard in the literature, resembles the effects of a news shock, and somewhat contrary to intuition suggests that the impact of the uncertainty shock on REITs returns were higher during the pre-GFC era.

We develop and study change point detection and estimation procedures for the covariance kernel of functional data based on the norms of a generally weighted process of partial sample estimates. It is shown under mild weak dependence and moment conditions on the data that in the absence of a change point a detector based on integrating such a process over the partial sample parameter is asymptotically distributed as the norm of a Gaussian process, which furnishes a consistent change point detection procedure. We further derive consistency and local asymptotic results for this detector in the presence of a change in the covariance function. The corresponding change point estimator based on such a process is also shown to be rate optimal for estimating an existing change point, and further is asymptotically distributed as the argument maximum of a Gaussian process under a local asymptotic framework. We study the detector and change point estimator in a small simulation study to detect changes in the covariance of functional autoregressive and generalized conditionally heteroscedastic processes, which demonstrate that the use of the weighted CUSUM statistics in this context generally improves performance over existing methods. These new statistics are demonstrated in an application to detecting changes in the volatility of high resolution intraday asset price curves derived from oil futures prices.

We investigate the puzzle of why bid–ask spreads of options are so large by focussing on the price impact component of the spread. We propose a structural vector autoregressive model for trades in the option market to analyze whether they move the underlying price and/or the underlying’s volatility. Our model captures cross-option strategies by pooling order flows across contracts after a decomposition into exposure to the underlying asset and its volatility. While our estimates confirm that S&P500 option trades indeed significantly move the underlying and the volatility, the economic magnitudes are very small. Hence, large bid–ask spreads of options remain a puzzle.

We explore the short-run forecasting problem at horizons of 1, 5, 10, 15, and 20 days for three forecasting periods within one year for the Chinese stock index from April 16, 2010, the launch date of the index futures, to May 19, 2014 with daily closing prices. We study forecast performance of 51 individual time series models that are different variations of autoregressive models, (Bayesian) vector autoregressive models, and (Bayesian) vector error correction models, and 41 composite models based on different trimming strategies of these individual models. The composite models, including the previous best forecast, equal-weighted average, inverse mean squared error, bias adjusted mean, shrinkage, and odds matrix approaches, utilize the idea of model boosting to diversify against possible mis-specifications, breaks, and structural changes in individual models, and aim at more robust performance. Across all forecasting horizons and forecasting periods investigated, we arrive at a shrinkage composite model with the shrinkage parameter of 0.25 that is optimal based on the mean squared error. This result is robust against the choice of futures series used in individual models and the pre-processing of structural breaks in data. We also discuss empirical findings at a more granular level, including comparisons of individual models and those of composite forecasts. Our results should fulfill different forecasting users’ information needs for decision making and policy analysis. The empirical framework also has potential of being adapted to similar time series forecasting problems in different fields.

COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible–Infected–Recovered model, Susceptible–Exposed–Infected–Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait’s gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson’s correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.

Probabilistic forecasting is becoming increasingly important for a wide range of applications, especially for energy systems such as forecasting wind power production. A need for proper evaluation of probabilistic forecasts follows naturally with this, because evaluation is the key to improving the forecasts. Although plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist, we find that there is a need for an introduction with some practical application. In particular, many forecast scenarios in energy systems are inherently multivariate, and while univariate evaluation methods are well understood and documented, only limited and scattered work has been done on their multivariate counterparts. This paper therefore contains a review of a selected set of probabilistic forecast evaluation methods, primarily scoring rules, as well as practical sections that explain how these methods can be calculated and estimated. In three case studies featuring simple autoregressive models, stochastic differential equations and real wind power data, we implement, apply and discuss the logarithmic score, the continuous ranked probability score and the variogram score for forecasting problems of varying dimension. Finally, the advantages and disadvantages of the three scoring rules are highlighted, and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems.

Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider long-term dependencies over several days in occupant behavior. An accurate mapping of long-term dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supply-side fluctuations of renewable based energy systems. The aim of this study is to bridge the gap between social practice theory, energy related activity modelling and novel machine learning approaches. The weaknesses of existing approaches are addressed by combining time use survey data with mobility data, which provide information about individual mobility behavior over periods of one week. In social practice theory, emphasis is placed on the sequencing and repetition of practices over time. This suggests that practices have a memory. Transformer models based on the attention mechanism and Long short-term memory (LSTM) based neural networks define the state of the art in the field of natural language processing (NLP) and are for the first time introduced in this paper for the generation of weekly activity profiles. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures long-term dependencies in mobility behavior. In a second step, an imputation model enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for multiple use cases one of which is modelling consistent electricity, heat and mobility demand profiles of households. The approach developed provides the basis for making high-quality weekly activity data available to the general public without having to carry out complex application procedures.

While baseline energy modelling approaches for buildings are progressing to meet the demand for measurement and verification (M&V) of savings of an energy performance contract, more robust and insightful modelling approaches are needed. This study compares four baseline modelling approaches (e.g., change point, nonlinear autoregressive exogenous models (NARX), Gaussian process, and ensembles of trees) in terms of predictive accuracy and operational insights. The approaches were selected based on preliminary study and employed on pre- and post-intervention data gathered from twelve commercial buildings in Ottawa, Canada. Changes in the post-intervention energy use were estimated by comparing the predicted data with measured data for the individual buildings. The known primary intervention in these buildings was implementing commercially available smart building technologies that identify and address operational suboptimalities. A deeper analysis of one building’s automation system data indicates that energy savings were largely due to simple corrective actions such as eliminating the concurrent operation of heating and cooling coils of an air handling unit. The results show that saving estimates are largely affected by the choice of baseline energy modelling approach. NARX outperformed all other modelling approaches regarding accuracy and data fitting capability. On the contrary, the change point modelling approach offered insights into the operational performance of the buildings.

The effectiveness of an orthogonal to backward mean transformation is investigated in the context of a non-stationary panel data model. It is shown that the corresponding estimator is as efficient as Transformed Maximum Likelihood when the autoregressive parameter is equal to unity. Furthermore, a recently introduced bias-corrected version is almost as efficient as the Pooled Least Squares estimator.

Seasonal influenza in the United States is estimated to cause 9–35 million illnesses annually, with resultant economic burden amounting to $47-$150 billion. Reliable real-time forecasts of influenza can help public health agencies better manage these outbreaks. Here, we investigate the feasibility of three autoregressive methods for near-term forecasts: an Autoregressive Integrated Moving Average (ARIMA) model with time-varying order; an ARIMA model fit to seasonally adjusted incidence rates (ARIMA-STL); and a feed-forward autoregressive artificial neural network with a single hidden layer (AR-NN). We generated retrospective forecasts for influenza incidence one to four weeks in the future at US National and 10 regions in the US during 5 influenza seasons. We compared the relative accuracy of the point and probabilistic forecasts of the three models with respect to each other and in relation to two large external validation sets that each comprise at least 20 other models.Both the probabilistic and point forecasts of AR-NN were found to be more accurate than those of the other two models overall. An additional sub-analysis found that the three models benefitted considerably from the use of search trends based 'nowcast' as a proxy for surveillance data, and these three models with use of nowcasts were found to be the highest ranked models in both validation datasets. When the nowcasts were withheld, the three models remained competitive relative to models in the validation sets. The difference in accuracy among the three models, and relative to models of the validation sets, was found to be largely statistically significant.Our results suggest that autoregressive models even when not equipped to capture transmission dynamics can provide reasonably accurate near-term forecasts for influenza. Existing support in open-source libraries make them suitable non-naïve baselines for model comparison studies and for operational forecasts in resource constrained settings where more sophisticated methods may not be feasible. Keywords: Influenza-like illness, Forecasts, Near-term incidence, Auto-regressive models, Neural networks

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5%–100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP. Keywords: Break size estimation, Loss of coolant accident, NARX neural network, Nuclear power plants, Accident management support tools

As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the air becomes greater than that of the strata. This cyclic phenomenon, also known as the “thermal damping effect” will continue throughout the year reducing the effect of surface air temperature variation. The objective of this paper is to quantify the thermal damping effect in vertical underground airways. A nonlinear autoregressive time series with external input (NARX) algorithm was used as a novel method to predict the dry-bulb temperature (Td) at the bottom of intake shafts as a function of surface air temperature. Analyses demonstrated that the artificial neural network (ANN) model could accurately predict the temperature at the bottom of a shaft. Furthermore, an attempt was made to quantify typical “damping coefficient” for both production and ventilation shafts through simple linear regression models. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable accuracy when predicting the Td at the bottom of intake shafts. Keywords: Underground mining, Vertical openings, Thermal damping effect, Artificial neural network, Nonlinear autoregressive with external input (NARX)

Highlights • The Grey and Vector autoregressive models are coupled to improve their accuracy. • Five economic and demographic parameters are included in the new hybrid model. • This new model is a reliable forecasting tool for assessing energy demand., Forecasting energy demand in general, and electricity demand in particular, requires the developing reliable forecasting tools that can be used to monitor the evolution of consumers’ energy needs more accurately. The proposed new hybrid GM(1,1)-VAR(1) model is meant for that purpose. The latter is based on the Grey and Vector autoregressive approaches, and makes it possible to predict future demand, by taking into account economic and demographic determinants with an exponential growth trend. With an associated APE of 1.5, a MAPE of 1.628%, and an RMSE of 15.42, this new model thus presents better accuracy indicators than hybrid models of the same nature. Also, it proves to be as accurate as some recent hybrid artificial intelligence models. The model is thus a reliable forecasting tool that can be used to monitor the evolution of energy demand.•The Grey and Vector autoregressive models are coupled to improve their accuracy.•Five economic and demographic parameters are included in the new hybrid model.•This new model is a reliable forecasting tool for assessing energy demand., Graphical abstract Image, graphical abstract

This study proposes a novel approach that investigates the dynamic dependency among exchange rates by extending time-varying copulas' parameters following an autoregressive moving average (ARMA) process. The process consists of an autoregressive part that explains the effect of the previous parameters and a forcing variable that measures the dependence structure between marginal variables. We apply this model to the daily data of the exchange rates of five Asian countries with the strongest economies before and during the 2020 pandemic, namely CNY/USD, IDR/USD, INR/USD, JPY/USD, and KRW/USD. The ARIMA-GARCH model was used to model the exchange rates data and estimate the dynamic dependence using time-varying copulas with the extended parameters. The dynamic dependencies between Chinas and the four countries' exchange rates before and during the 2020 pandemic was evidenced. Moreover, India is the country whose exchange rate has been most strongly affected by the pandemic. Some of the highlights of the proposed approach are:•This paper provides two algorithms to investigate the dynamic dependencies among exchange rates data during a crisis and forecast the data using time-varying copulas with the extended parameters.•There are four extended time-varying copulas' parameters which can measure the dynamic dependencies between variables.•The computation procedure is easy to implement., Graphical Abstract Image, graphical abstract

The progenies of international bulls in diverse climatic conditions and management levels may lead to different expressions of their genetic potential resulting in a re-ranking of these bulls. Therefore, evaluate the presence of genotype by environment interaction (G × E) within and across countries is important to guide the decision-making on alternative selection strategies. Thus, a two-step reaction norm (RN) approach was used to investigate the presence of G × E in Portuguese and Brazilian Holstein cattle. In step 1, we performed a within-country genetic evaluation using an autoregressive model to obtain precorrected phenotypes and environmental gradients (herd test-day solutions, HTD levels). In step 2, the precorrected phenotypes were considered as two distinct traits in a bi-trait RN model to estimate variance components across HTD levels, genetic correlation between HTD levels in Portugal and Brazil, and RN of the estimated breeding values. Additionally, the genetic correlation between countries using a bi-trait random regression (RR) sire model was obtained. In step 1, genetic additive variance for milk yield (MY) in Portugal was 14.1% higher than in Brazil. For somatic cell score (SCS), the genetic additive variance in Portugal was 12.7% lower than in Brazil. Although similar heritability estimates for SCS were observed in both countries, MY heritabilities were 0.31 for Portugal and 0.23 for Brazil. Genetic correlations (SD) between both countries obtained using RR sire model were 0.78 (0.051) for MY and 0.75 (0.062) for SCS. In step 2, MY genetic correlations among HTD levels within countries were higher than 0.94 for Portugal and 0.98 for Brazil. Somatic cell score genetic correlations among HTD levels ranged from 0.70 to 0.99 for Portugal and from 0.84 to 0.99 for Brazil. The average (SD) of genetic correlation estimates between Portuguese and Brazilian HTD levels were 0.74 (0.009) for MY and 0.57 (0.060) for SCS. These results suggest the presence of G × E for MY and SCS of Holstein cattle between both countries. Although there was no indication of G × E between Brazilian herd environments, the low genetic correlation for SCS indicates potential re-ranking of bulls between extreme environmental gradient in Portugal. Overall, the results of this study evidence the importance of national and international genetic evaluation systems to assist dairy farmers in the selection of the best genotypes to obtain the expected returns from investments in imported semen and to realize genetic progress in dairy populations under local environmental conditions.

Summary Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior., Graphical abstract, Highlights • Development of several lifetime models based on long-term cell aging dataset • Models validation by WLTC cycling and based on realistic scenarios • Models assessment in terms of accuracy, computational effort, applicability, etc. • Pros and cons of the best lifetime model toward realistic use, Electrochemistry; Electrochemical Energy Storage; Energy Engineering; Energy Storage; Energy Systems; Energy Materials

The development of procedures based on fractional calculus is an emerging research area. This paper presents a new perspective regarding the fractional least mean square (FLMS) adaptive algorithm, called multi innovation FLMS (MIFLMS). We verify that the iterative parameter adaptation mechanism of the FLMS uses merely the current error value (scalar innovation). The MIFLMS expands the scalar innovation into a vector innovation (error vector) by considering data over a fixed window at each iteration. Therefore, the MIFLMS yields better convergence speed than the standard FLMS by increasing the length of innovation vector. The superior performance of the MIFLMS is verified through parameter identification problem of input nonlinear systems. The statistical performance indices based on multiple independent trials confirm the consistent accuracy and reliability of the proposed scheme., The authors are thankful to Dr. Ivan Markovsky for allowing us to use the results of the real experimentations conducted at the Southampton University [44–45]. This work was supported by the National Natural Science Foundation of China under Grant No. 51977153, 51977161, 51577046, the State Key Program of National Natural Science Foundation of China under Grant No. 51637004, National Key Research and Development Plan ”important scientific instruments and equipment development” Grant No. 2016YFF010220 and Equipment research project in advance Grant No 41402040301.

Hong Kong is located at the Pearl River Delta, which is among the urban areas that are most vulnerable to natural disasters in the world. The research suggests that environmental factors such as climate factors can have significant effects on the performance of stock markets. The aim of this chapter is thus to explore the effects of natural disasters on the stock market return and volatility in Hong Kong, over various event windows from 1 day to 2 months postdisaster. An ARMAX (autoregressive moving average with exogenous regressor)–EGARCHX (exponential generalised autoregressive conditional heteroskedasticity with exogenous regressor) model, which is an augmentation of a standard ARMA process with an EGARCH (exponential generalized autoregressive conditional heteroskedastic) process and an intervention variable X, was used for empirical analysis. The results reveal that natural disasters have adverse effects on the Hong Kong Stock Market return and volatility with increasing magnitude. However, the adverse effects only survive for a short period of up to 12 days after the events before dying out afterward. In addition, the presence of asymmetric volatility indicates that market return volatility is higher during extreme weather events. Higher return volatility induces a higher probability of a bear market.

This chapter aims to introduce several multiple/multivariate linear and nonlinear time series models for forecasting streamflow process, which have widely been used in econometrics earlier. However, a few studies regarding the application of these models have been carried out in modern hydrology in recent years. For this purpose, to model conditional mean behavior of multiple streamflow time series as well as multivariate rainfall-runoff process, vector autoregressive without/with exogenous variables approach as the “multiple/multivariate linear” time series models were introduced. In addition, to capture conditional time-varying variance that may exist in the hydrologic time series, the bivariate diagonal vectorization heteroscedasticity model was applied. This model is one of the main multiple/multivariate generalized autoregressive conditional heteroscedasticity “nonlinear” time series models. In this regard, two recent studies of Iran were described in this chapter by applying the aforementioned time series models in forecasting streamflows in more detail.

To date, most reviews of trust in human-robot interaction have focused on qualitative models. Our review provides a descriptive account of computational models which have been applied and empirically evaluated. We applied metaanalytic methods to search for studies which developed quantitative models of human-robot trust in which trust was inferred based on physiological or behavioral measures, and which ultimately culminated in an evaluative study with human participants. We found 38 articles, 14 of which evaluated subjective trust and human-robot team performance. The models we identified can broadly be grouped into three categories based on model type: linear autoregressive models, dynamic Bayesian networks, and decision-analytical models. These were mostly applied to problems of adaptive autonomy and function allocation, although some were used to change the kinematics of robot manipulators as in the case of collaborative physical interaction. The domains to which these models were applied include the military, surveillance, autonomous vehicles, and manufacturing.

Variability and changes in personality constructs are frequently assessed through longitudinal data. Measurement error, which is ubiquitous in psychology, can lead to bias in the measurement of change. Therefore, personality researchers frequently use latent variable models to analyze longitudinal data. Latent variable models correct for unreliability in the measured scores. In this chapter, we review different classes of longitudinal confirmatory factor approaches, including models for measuring short-term variability (latent state and state–trait models) and models for measuring more long-lasting trait changes (latent autoregressive, latent change, and latent growth curve models). Throughout our presentation, we highlight the flexibility of models for multiple indicators per measurement occasion when it comes to testing for measurement equivalence and examining variable-specific effects. We also present applications to a four-wave (12-month intervals) data set on oppositional defiant disorder and callous/unemotional traits in children.

We examine stock return autocorrelation at various quantiles of the returns' distribution and use it to forecast stock return volatility. Our empirical results show that the strength of the autoregression varies across the quantiles of the returns' distribution in terms of both magnitude and persistence. Specifically, the autoregression order and magnitude of the coefficients is lower in the left tail in comparison with the right tail. Additionally, we show that the quantile autoregressive (QAR) framework proposed in this study improves out-of-sample volatility forecasting performance compared to the generalised autoregressive conditional heteroscedasticity (GARCH)-type models and other quantile-based models. We also observe greater outperformance in QAR estimates during periods of financial turmoil. Moreover, the QAR method also explains the stylized ‘leverage effect’ associated with asset returns in the presence of volatility asymmetry.

While the interest of the transport research community and automotive industry is increasingly turning towards developments and improvements in the field of autonomous vehicles, there is a need for a better understanding of the end users’ preferences regarding perceived passenger comfort, in order to improve acceptance and intention to use. The present study is based on a driving simulator experiment conducted at the University of Leeds Driving Simulator and approaches the issue of comfort via observed speed choice behaviour. Participants drove a series of driving simulator scenarios composed of road segments of different road type, road geometry, risk level at the road edge, and oncoming traffic. They also completed a series of self-report questionnaires, including Arnett’s Inventory of Sensation-seeking. A set of models was developed in order to investigate the effects of road environment and sensation-seeking on speed behaviour. The initial model only considered explanatory variables related to the road environment and accounted for individual unobserved heterogeneity. Past behaviour, serial correlation and heterogeneity in road environment were then introduced in the model specification. The autoregressive disturbance term that accounted for serial correlation was also applied in the form of a random variable and significantly improved model fit. Finally, sensation-seeking was incorporated in the model as a latent variable. The results showed a significant impact of most of the road elements as road type, curvature, risk type at the road edge on observed behaviour, implying a future need for the development of autonomous vehicle controllers that adapt their performance based on the road environment. Moreover, sensation-seeking had a significant and positive effect on speed, which indicates a potential future demand for personalised controllers to meet the users’ individual preferences.

A new method for estimating Bayesian vector autoregression (VAR) models using priors from a dynamic stochastic general equilibrium (DSGE) model is presented. The DSGE model priors are used to determine the moments of an independent Normal-Wishart prior for the VAR parameters. Two hyper-parameters control the tightness of the DSGE-implied priors on the autoregressive coeficients and the residual covariance matrix respectively. Selecting the values of the hyper-parameters that maximize the marginal likelihood of the Bayesian VAR provides a method for isolating subsets of DSGE parameter priors that are at odds with the data. The ability of the new method to correctly detect misspecified DSGE priors is illustrated using a Monte Carlo experiment. The method gives rise to a new `quasi-Bayesian' estimation approach: posterior estimates of the DSGE parameter vector can be recovered from the BVAR posterior estimates. An empirical application on US data reveals economically meaningful differences in posterior parameter estimates when comparing the quasi-Bayesian estimator with Bayesian maximum likelihood. The new method also indicates that the DSGE prior implications for the residual covariance matrix are at odds with the data.

Background‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015)MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive model to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) and parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD metric is compared with non-parametric Granger causality (NPG), a well-established methodology for model free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate degrees of instantaneous signal mixing.ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.HighlightsNon-parametric directionality (NPD) is a novel directed connectivity metric.NPD estimates are equivalent to Granger causality but more robust to signal confounds.Multivariate extensions of NPD can correctly identify signal routing.AbbreviationsdFCDirected functional connectivityEEGElectroencephalogramLFPLocal field potentialMEGMagnetoencephalogramMVARMultivariate autoregressive (model)NPDNon-parametric directionalityNPGNon-parametric Granger (causality)SMASupplementary motor areaSNRSignal-to-noise ratioSTNSubthalamic Nucleus

In this study, the volume of dirty money in Iran was estimated. The data belonged to the period of 1997-2019, and was taken from the Central Bank of Iran (website: https://www.cbi.ir). Fuzzy logic was used to estimate the underground economy. Fuzzy theory can mathematically formulate many variables that are imprecise and ambiguous concepts. This theory is appropriate for reasoning, inference, control, and decision-making under uncertainty. This approach works in conditions of uncertainty. In cases in which the variables are inaccurate, this method is used. Fuzzy set theory is a generalization of the set theory. The underground economy is important in estimating the amount of dirty money and has a positive effect on this amount. The effect of the underground economy was investigated using the vector autoregressive (VAR) and vector error correction (VECM) models.•In this article applied the fuzzy logic, to estimate the underground economy.•The method presented in this article can be useful for Researchers and managers in the monetary trend of economics.•The fuzzy method is the best way to estimate the size of the underground economy because it is a measure of uncertainty., Graphical abstract Image, graphical abstract

Matrix $\mathbf{C}$ can be blindly deconvoluted if there exist matrices $\mathbf{A}$ and $\mathbf{B}$ such that $\mathbf{C}= \mathbf{A} \ast \mathbf{B}$, where $\ast$ denotes the operation of matrix convolution. We study the problem of matrix deconvolution in the case where matrix $\mathbf{C}$ is proportional to the inverse of the autocovariance matrix of an autoregressive process. We show that the deconvolution of such matrices is important in problems of Hankel structured low-rank approximation (HSLRA). In the cases of autoregressive models of orders one and two, we fully characterize the range of parameters where such deconvolution can be performed and provide construction schemes for performing deconvolutions. We also consider general autoregressive models of order $p$, where we prove that the deconvolution $\mathbf{C}= \mathbf{A} \ast \mathbf{B}$ does not exist if the matrix $\mathbf{B}$ is diagonal and its size is larger than $p$.

Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time. Keywords: COVID-2019 epidemic, ARIMA model, Forecast, Infection control

Spurious (nonsensical) regressions with independent random walks or even with stationary series are well known. However, how their spuriosity is affected by nonlinearity in series has been scantly addressed. In this study, I examine, using Monte Carlo analysis, the effect of autoregressive conditional Heteroskedasticity (ARCH) on nonsensical regressions and I find that ARCH can neutralize most of spuriosity. Specifically, my analysis of finite sample behavior of the t-ratio in a spurious regression framework where ARCH effects are included in a Data Generating Process (DGP) model and Monte Carlo experiments show that large ARCH effects somehow weaken the degree of spuriosity. This will have implications for unit root and cointegration analysis. My simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious. Keywords: Monte Carlo analysis, Nonsensical regressions, Data Generating Process, Stationary and non-stationary series, ARCH, JEL Classification: C22, C81, D12

We derive a closed-form expression for the finite predictor coefficients of multivariate ARMA (autoregressive moving-average) processes. The expression is given in terms of several explicit matrices that are of fixed sizes independent of the number of observations. The significance of the expression is that it provides us with a linear-time algorithm to compute the finite predictor coefficients. In the proof of the expression, a correspondence result between two relevant matrix-valued outer functions plays a key role. We apply the expression to determine the asymptotic behavior of a sum that appears in the autoregressive model fitting and the autoregressive sieve bootstrap. The results are new even for univariate ARMA processes., Journal of Multivariate Analysis, to appear

Objective: The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies. Methods: We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes. Results: A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from −0.9 to 0.9, with various effect sizes. The power to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models. Conclusion: This article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented. Keywords: Policy evaluation, Interrupted time series, Count outcomes, Segmented regression, Quasi-experimental design, Power, Sample size calculation

Methods for incorporating high resolution intra-day asset price data into risk forecasts are being developed at an increasing pace. Existing methods such as those based on realized volatility depend primarily on reducing the observed intra-day price fluctuations to simple scalar summaries. In this study, we propose several methods that incorporate full intra-day price information as functional data objects in order to forecast value at risk (VaR). Our methods are based on the recently proposed functional generalized autoregressive conditionally heteroscedastic (GARCH) models and a new functional linear quantile regression model. In addition to providing daily VaR forecasts, these methods can be used to forecast intra-day VaR curves, which we considered and studied with companion backtests to evaluate the quality of these intra-day risk measures. Using high-frequency trading data from equity and foreign exchange markets, we forecast the one-day-ahead daily and intra-day VaR with the proposed methods and various benchmark models. The empirical results suggested that the functional GARCH models estimated based on the overnight cumulative intra-day return curves exhibited competitive performance with benchmark models for daily risk management, and they produced valid intra-day VaR curves.

This longitudinal study examines the impact that terrorist attacks within a representative group of European countries can have on the tourism demand of a South European country with no record of terrorism attacks. In order to analyze the connections between terrorist attacks and tourists' arrivals, occurred between 2002 and the end of 2016, an Unrestricted Vector Autoregressive model was used for multivariate time series analysis. The main results show that terrorist attacks have a strong impact on tourist arrivals and confirm the existence of terrorism spillover, namely the substitution and generalization effects phenomena., Highlights • It is a contribution to existing research on spillover effects of terrorism on tourism. • It is the first study on terrorism effects on a country with no record of terrorism. • A Unrestricted Vector Autoregressive model analyzed terrorism impacts on arrivals on multivariate time series. • Results show substitution and generalization effects caused by terrorism. • Results confirm the existence of a consumer short memory effect.

The operation status of a process in the steel industry is mainly defined by three aspects, efficiency, productivity and safety. It provides guidance for the operators to make decisions on their future actions. The abrasive process environment inside a blast furnace (BF) makes it demanding to analyse the operation status by direct internal measurements. The blast furnace gas utilization factor (ETACO) is an essential indicator of the process efficiency. Besides efficiency, productivity and safety can, to some extent, be derived from the pressure drop (DP) and the top gas temperature (TG). This paper presents a nonlinear autoregressive network with exogenous inputs (NARX) model for the simultaneous multistep ahead prediction of ETACO, DP and TG, based upon a new set of fast and slow dynamic input attributes. Validation results using real industrial plant measurements show that this approach not only enables monitoring of the current operation status but also provides prediction capability by including the slow dynamics of the blast furnace into the model.