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Longitudinal measurement invariance (LMI) is a critical prerequisite to assessing change over time with intensive longitudinal data (ILD). For LMI testing with ILD, we propose cross-classified factor analysis (CCFA) to detect non-invariant item parameters and alignment optimization (AO) to detect non-invariant time points as a supplement to CCFA. In addition, we use a covariate in CCFA to identify a source of non-invariance. To evaluate the proposed models under unique features of ILD, such as autoregression (AR), we conducted a Monte Carlo simulation study. The results showed CCFA can be an excellent tool for ILD LMI testing regardless of simulation factors even when AR was misspecified and can identify a source of non-invariance using a covariate. AO can supplement CCFA to find non-invariant time points although AO requires a large number of persons. We provide detailed discussions and practical suggestions. [ABSTRACT FROM AUTHOR]

Unified structural equation modeling (uSEM) implemented in the group iterative multiple model estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: (a) estimating autoregressive relations by default in the null model (GIMME-AR) and (b) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best--and were roughly equivalent in performance--when autoregressive relations were strong (e.g., b = .60). When they were weak (b # .10), GIMME-MS displayed an advantage, although overall directionality recovery was modest. Analyses of empirical data in which autoregressive relations were characteristically strong (resting state functional MRI) versus weak (daily diary) mirrored simulation results and confirmed that these strategies can disagree on directionality when autoregressive relations are weak. Findings have important implications for psychological and neuroimaging applications of uSEM/GIMME and suggest specific scenarios in which researchers might or might not be confident in directionality results. [ABSTRACT FROM AUTHOR]

Relatively large deformations of the steel arch support in underground coal mines in the Republic of Serbia present one of the main problems for achieving the planned production of coal. Monitoring of the critical sections of the steel arch support in the underground roadways is necessary to gather quality data for the development of a forecasting model. With a new generation of 3D laser scanners that can be used in potentially explosive environments (ATEX), deformation monitoring is facilitated, while the process of collecting precise data is much shorter. In this paper, we used a combination of grey and stochastic system theory combined with an autoregressive process for processing collected data and the development of a forecasting model of the deformations of the steel arch support. Forecasted data accuracy based on the positions of the markers placed along the internal rim of support construction shows high accuracy with MAPE of 0.2143%. The proposed model can successfully be used by mining engineers in underground coal mines for steel arch support deformations prediction, consequentially optimizing the maintenance plan of the underground roadways and achieving planned production. [ABSTRACT FROM AUTHOR]

Computer technology has developed to a very advanced measure. Calculations using complex formulas are no longer an obstacle for industry and researchers. Along with advances in computing technology, the development of fuzzy system models is also experiencing rapid progress. This paper proposes a fuzzy model combined with Vector Autoregression. The fuzzy membership function is built by selecting the median of each set to be the center of the fuzzy set. The function chosen as the membership function is Gaussian. The fuzzy Vector Autoregression model obtained was applied to the Farmer's Exchange Rate in Central Java Province. The accuracy of the model is measured based on the Mean Absolute Percentage Error. The results of model trials on FER Central Java in 2014-2020, show a pretty good forecast, namely forecasting with MAPE around 5%, and not exceeding 10%.

The focus of Sustainable Development Goal is to provide opportunities for lifelong learning and equip individuals with practical skills through high-quality technical and vocational education. This study delves into the connection between vocational education and economic development and growth in India, with a focus on employment, capital formation, and productivity. Using an autoregression analysis and statistical models, the research examines the long-term impact of vocational education on these variables from 1993 to 2021. The results show that there is a significant and substantial link between vocational and skill development education and production, and between vocational and skill development and capital formation. The study advised that increasing access to vocational education can lead to economic development and growth and contribute to human development in India by boosting capital formation. [ABSTRACT FROM AUTHOR]

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

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

This paper describes a source tracking technique in a reverberant environment using a new combination of an adaptive species-based particle swarm optimization (ASPSO) algorithm and a multiple signal classification (MUSIC) algorithm. To mitigate the effects of reverberation, an insightful dereverberation method based on an online autoregressive (AR) array and a minimum variance distortionless response (MVDR) beamformer is developed to dereverberate the microphone signal prior to direction of arrival (DOA) estimation using MUSIC. On the basis of several evolutionary schemes, ASPSO enables rapid tracking by finding local maxima in the MUSIC pseudospectrum. In the ASPSO algorithm, particles are divided into different species, where each species is associated with a sound source. As the sound source moves, the DOA information is dynamically updated using ASPSO, in which the inertia weight decreases progressively to prevent premature convergence. Two update rules for adapting the filter coefficients are employed for drastically moving sources. Simulations and experiments are conducted using a circular microphone array to validate the proposed ASPSO with AR (ASPSO-AR) algorithm. The results demonstrate that ASPSO-AR requires one-third of the processing time of the grid search (GS) method. In addition, the root-mean-square error (RMSE) of the ASPSO-AR algorithm is 10° less than that of the GS method.

THE PURPOSE. Smart electrical grids involve extensive use of information infrastructure. Such an aggregate cyber-physical system can be subject to cyber attacks. One of the ways to counter cyberattacks is state estimation. State Estimation is used to identify the present power system operating state and eliminating metering errors and corrupted data. In particular, when a real measurement is replaced by a false one by a malefactor or a failure in the functioning of communication channels occurs, it is possible to detect false data and restore them. However, there is a class of cyberattacks, so-called False Data Injection Attack, aimed at distorting the results of the state estimation. The aim of the research was to develop a state estimation algorithm, which is able to work in the presence of cyber-attack with high accuracy.METHODS. The authors propose a Multi-Model Forecasting-Aided State Estimation method based on multi-model discrete tracking parameter estimation by the Kalman filter. The multimodal state estimator consisted of three single state estimators, which produced single estimates using different forecasting models. In this paper only linear forecasting models were considered, such as autoregression model, vector autoregression model and Holt’s exponen tial smoothing. When we obtained the multi-model estimate as the weighted sum of the single-model estimates. Cyberattack detection was implemented through innovative and residual analysis. The analysis of the proposed algorithm performance was carried out by simulation modeling using the example of a IEEE 30-bus system in Matlab.RESULTS. The paper describes an false data injection cyber attack and its specific impact on power system state estimation. A Multi - Model Forecasting-Aided State Estimation algorithm has been developed, which allows detecting cyber attacks and recovering corrupted data. Simulation of the algorithm has been carried out and its efficiency has been proved.CONCLUSION. The results showed the cyber attack detection rate of 100%. The Multi-Model Forecasting-Aided State Estimation is an protective measure against the impact of cyber attacks on power system.

This study empirically analyzes time series momentum (TSM) in the European equity market between 2000 & 2020. The study produces additional evidence on TSM where a significant and persistent market price anomaly enables investors to earn abnormal returns. To achieve this goal the present study implements a pooled autoregressive model to test the predictability power of European equity indices of future returns. The results indicate that strategies based on TSM are in line with the discussed literature and enable market agents to earn returns above the market (0.71% per month) by using a six-factor model.

Summary: The discrete minification model based on the modified negative binomial operator, as an extension to the continuous minification model, can be used to describe an extreme value after few increasing values. To make this model more practical and flexible, a new minification integer‐valued autoregressive process driven by explanatory variables is proposed. Ergodicity of the new process is discussed. The estimators of the unknown parameters are obtained via the conditional least squares and conditional maximum likelihood methods, and the asymptotic properties are also established. A testing procedure for checking existence of the explanatory variables is developed. Some Monte Carlo simulations are given to illustrate the finite‐sample performances of the estimators under specification and misspecification and the test, respectively. A real example is applied to illustrate the performance of our model. [ABSTRACT FROM AUTHOR]

This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule–Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints m and the series length N satisfy m / N → 0 as N → ∞ . [ABSTRACT FROM AUTHOR]

We propose an extension of the N-mixture model that enables the estimation of abundances of multiple species as well as the correlations between them. Our novel multi-species N-mixture model (MNM) is the first to address the estimation of both positive and negative inter-species correlations, which allows us to assess the influence of the abundance of one species on another. We provide extensions that permit the analysis of data with excess of zero counts, and relax the assumption that populations are closed through the incorporation of an autoregressive term in the abundance. Our approach provides a method of quantifying the strength of association between species' population sizes and is of practical use to population and conservation ecologists. We evaluate the performance of the proposed models through simulation experiments in order to examine the accuracy of both model estimates and coverage rates. The results show that the MNM models produce accurate estimates of abundance, inter-species correlations and detection probabilities at a range of sample sizes. The MNM models are applied to avian point data collected as part of the North American Breeding Bird Survey between 2010 and 2019. The results reveal an increase in Bald Eagle abundance in south-eastern Alaska in the decade examined. [ABSTRACT FROM AUTHOR]

To reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent crimes are discussed. By taking campus violence cases as examples, the major influencing factors of violent crimes are found through data analysis. The results show that campus violence cases are usually caused by the complex surrounding environments and persons. Also, campus security measures only cover a small range, and the security management is difficult. In the meantime, due to the younger ages and lack of self-protection awareness, students may easily become the targets of criminals. Therefore, the results have a positive significance for authorities to analyse the crime rates in a determinate area and take preventive measures against violent crimes.

To study urban air quality, this paper proposes a novel categorical time series model, which is based on a linear combination of bounded Poisson distribution and discrete distribution to describe the dynamic and systemic features of air quality, respectively. Daily air quality level data of three major cities in China, including Beijing, Shanghai and Guangzhou, are analyzed. It is concluded that the air quality in Beijing is the worst among the three cities but is gradually improving, and its dynamics is also the most pronounced. Theoretically, the design of our model increases the flexibility of the probabilistic structure while ensuring a dynamic feedback mechanism without high computational stress. We estimate the parameters through an adaptive Bayesian Markov chain Monte Carlo sampling scheme and show the satisfactory finite sample performance of the model through simulation studies. [ABSTRACT FROM AUTHOR]

Acknowledging a considerable literature on modeling daily temperature data, we propose a multi-level spatiotemporal model which introduces several innovations in order to explain the daily maximum temperature in the summer period over 60 years in a region containing Aragón, Spain. The model operates over continuous space but adopts two discrete temporal scales, year and day within year. It captures temporal dependence through autoregression on days within year and also on years. Spatial dependence is captured through spatial process modeling of intercepts, slope coefficients, variances, and autocorrelations. The model is expressed in a form which separates fixed effects from random effects and also separates space, years, and days for each type of effect. Motivated by exploratory data analysis, fixed effects to capture the influence of elevation, seasonality, and a linear trend are employed. Pure errors are introduced for years, for locations within years, and for locations at days within years. The performance of the model is checked using a leave-one-out cross-validation. Applications of the model are presented including prediction of the daily temperature series at unobserved or partially observed sites and inference to investigate climate change comparison. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

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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.

Due to the fact that the autocorrelation of time series of passenger demand under normal conditions is, as a rule, practically undeveloped, traditional forecasting methods based on taking into account autocorrelation dependences are not effective enough. The article proposes a direct accounting of the main factor affecting the accuracy of forecasting, namely the factor of seasonal heterogeneity of demand. This accounting is made on the basis of polynomial regression for the time dependence of demand. A specific design example demonstrates the comparative advantages of this approach to assessing the forecast of demand for rail transport.The regression approach is applied to the weekly averaged demand metrics for the time domain, where these metrics are considered known from the sales history. If there is a weekly demand heterogeneity in the forecast zone, an algorithm is proposed to restore such heterogeneity from the initial data.The forecast accuracy based on the proposed method is compared with the results achieved on the basis of the ARIMA model, which reveals, according to preliminary estimates, fairly high accuracy parameters. It is shown on the calculated examples that for the series of demand, which can be considered typical for the sphere of passenger traffic, the regression approach gives the forecast accuracy higher than the ARIMA model. The reasons are considered, due to which, for typical series of passenger demand, the regression approach can be considered as more promising than methods that include taking into account autocorrelation.

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

Effective responses to nuclear accidents require accurate and fast predictions of radionuclide transport in the atmosphere. However, real-time forecasting has been challenging, since predicting the transport requires meteorological forecasting, which relies on computationally intensive numerical weather simulations. To address this challenge, we have developed statistical emulators for the Weather Research and Forecasting model (WRF) to forecast wind and temperature fields around the source location. The emulator is constructed based on pre-computed WRF simulation results and their boundary conditions from global circulation models (GCM), enabling the rapid prediction of local-scale wind/temperature fields by ingesting the GCM-predicted boundary conditions available online for the next several days. We have explored two methods: (1) deep neural network and (2) a vector autoregressive model with exogenous variables (VARX). A Gaussian plume model is then used to take these field as input and to predict the distribution of radioactive material/dose in the environment. The results show that both emulators are capable of forecasting the wind field for several days in the future in real-time, while not compromising accuracy. The autoregressive model has shown a strong performance of predicting the temperature field, owing to the strong temporal autocorrelations in the temperature data. This framework offers a powerful tool for informed decision-making during emergencies such as enabling meteorology-informed timing of containment venting, and real-time guidance of evacuation strategies. • Weather forecasting emulators are developed and integrated with Gaussian plume model. • The emulators trained on historical data and allow the plume prediction in seconds. • The emulators are expected to become a key tool for nuclear emergency responses. [ABSTRACT FROM AUTHOR]

Relatively large deformations of the steel arch support in underground coal mines in the Republic of Serbia present one of the main problems for achieving the planned production of coal. Monitoring of the critical sections of the steel arch support in the underground roadways is necessary to gather quality data for the development of a forecasting model. With a new generation of 3D laser scanners that can be used in potentially explosive environments (ATEX), deformation monitoring is facilitated, while the process of collecting precise data is much shorter. In this paper, we used a combination of grey and stochastic system theory combined with an autoregressive process for processing collected data and the development of a forecasting model of the deformations of the steel arch support. Forecasted data accuracy based on the positions of the markers placed along the internal rim of support construction shows high accuracy with MAPE of 0.2143%. The proposed model can successfully be used by mining engineers in underground coal mines for steel arch support deformations prediction, consequentially optimizing the maintenance plan of the underground roadways and achieving planned production.

Forecasting the spread of COVID-19 infection is an important aspect of public health management. In this paper, we propose an approach to forecasting the spread of the pandemic based on the vector autoregressive model. Concretely, we combine the time series for the number of new cases and the number of new deaths to obtain a joint forecasting model. We apply the proposed model to forecast the number of new cases and deaths in the UAE, Saudi Arabia, and Kuwait. Test results based on out-of-sample forecast show that the proposed model achieves a high level of accuracy that is superior to many existing methods. Concretely, our model achieves mean absolute percentage error (MAPE) of 0.35%, 2.03%, and 3.75% in predicting the number of daily new cases for the three countries, respectively. Furthermore, interpolating our predictions to forecast the cumulative number of cases, we obtain MAPE of 0.0017%, 0.002%, and 0.024%, respectively. The strong performance of the proposed approach indicates that it could be a valuable tool in managing the pandemic. [ABSTRACT FROM AUTHOR]

Renewable energy integration becomes an important criterion for the sustainable generation of electrical power. The high introduction of wind power plants into the power system leads to some inconveniences in the power system operators' work due to the unpredictable and variable nature of the wind speed and the power generated by wind farms. Even though the power generated at the wind power plants is not regulated by the system operator, the accurate predicting of the wind speed and the angle of its direction could solve such problems leading to improving the reliability of power supply systems. This paper considers the prediction of both the speed and direction of wind flows for the Far East coast. It is proposed to use autoregression based on the fuzzy systems concept. The goal of this study is to find a regression model that satisfies all observable fuzzy data within the specified optimality criterion. Thus, the regression coefficients are fuzzy numbers that can be expressed as interval numbers with membership values. As result, the electric power generated by wind power plants could provide additional energy accumulation function, if the wind flow velocity fluctuations will be considered. • A novel predicting method of wind speed was proposed based on a fuzzy regression. • The developed method allows assessing the maximum power output of a wind turbine. • The obtained computation results showed acceptable accuracy 3 h ahead. • The consideration of the wind flow velocity fluctuations provides additional energy accumulation in the wind power plants. • The proposed method allows to increase the reliability of the power supply. [ABSTRACT FROM AUTHOR]

The life form's genome contains all the hereditary data encoded by DNA sequences in medicinal database. Clarifying the genome is vital to decide the endurance, improvement and multiplication of a creature. As of late, entire genome groupings develop dramatically over the long haul. Likewise, to pack an enormous genome, we really want a substantial calculation and a legitimate calculation. In this point, the previous framework addresses a technique in light of programmed relapse demonstrating, the model not set in stone by particle swarm optimization (PSO) and the low pressure proportion for positive thinking of the PSO part. To improve, Hybrid streamlining of hospitalization-subordinate DNA pressure (HOARADNACOM) has been proposed and utilizes delicate registering strategies. This calculation works in even mode utilizing an AR-based replacement measurements strategy, and the model not entirely settled by the modified FireFly Algorithm (MFR). The essential firefly calculation fixes fireflies on the grounds that the most brilliant sparkles move haphazardly, bringing about horrible showing at specific emphases. If this splendid FireFluster can move in the improved bearing to that splendor, the exhibition of the calculations related with the best worldwide answers for all arrangements will endure. There isn't anything in this beneficial arrangement. The proposed framework expects to accomplish a bigger pressure proportion, alongside the usefulness of the succession got from pressure, which favors distance to diminish capacity, investigation, transmission upward, and air structure. Execution results show that the proposed MFF calculation can accomplish preferred pressure proportions over existing exploration techniques. [ABSTRACT FROM AUTHOR]

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

Rapid industrialization and urbanization are rapidly deteriorating ambient air quality, especially in the developing nations. Air pollutants impose a high risk on human health and degrade the environment as well. Earlier studies have used machine learning (ML) and statistical modeling to classify and forecast air pollution. However, these methods suffer from the complexity of air pollution dataset resulting in a lack of efficient classification and forecasting of air pollution. ML-based models suffer from improper data preprocessing, class imbalance issues, data splitting, and hyperparameter tuning. There is a gap in the existing ML-based studies on air pollution due to improper data handling and optimization. The present investigation aims to bridge these gaps and aid in effective air pollution classification and forecasting. Five ML models were developed, including one novel model named SMOTEDNN (Synthetic Minority Oversampling Technique with Deep Neural Network) to address air pollution classification. All five models utilized efficient data pre-processing and rigorous hyperparameter optimization. Three forecasting models were developed to forecast air pollution for one step-index based on statistical autoregression. All developed models in present investigation showed higher accuracy. Significantly, the novel model SMOTEDNN achieved an accuracy of (99.90%) higher than the other models from the current investigation and previous studies. [ABSTRACT FROM AUTHOR]

The empirical relationship between public expenditure and economic growth can be analysed from different viewpoints. This study focuses on the empirical testing of the validity of Wagner's law for the Indonesian economy. The high economic growth in the sample period of 1980–2014 makes the law likely to be applicable to Indonesia. Causality and cointegration techniques are used. A key finding in our vector autoregression analysis is unidirectional causality running from GDP and price to government expenditure, supporting Wagner's law. In the case of price and government expenditure, this study also finds evidence of a long-run cointegrating relationship, which appears stable and supports unidirectional causality. The vast majority of the deviations from the equilibrium relationship between government expenditure and price are found to be transitory shocks to government expenditure. Most deviations also appear significantly countercyclical to economic activity, suggesting that government expenditure does play a role in economic stabilisation. [ABSTRACT FROM AUTHOR]

This paper explores how human development responds to selected macroeconomic shocks in Nigeria. The study employed the Sen's capabilities approach as the analytical approach and posited that the level of education, health status, quality of investment, technology, and government fiscal and monetary policies are plausible determinants of human development. We used the Structural Vector Autoregression (SVAR) to estimate the responses of such selected shocks, which are inflation, interest rate, government capital expenditure, exchange rate, current account balance, and savings shocks. The Forecast Error Variance Decomposition (FEVD) and the Impulse Response (IR) showed that a fiscal policy shock is the major factor influencing human development outcomes. This finding underscored the important role government plays in enhancing the well-being of its citizens. Fiscal policy tools (such as investment in education, health, housing, and infrastructure) are essential for human development. In particular, the human development outcome is found to respond positively to shocks from real interest rates, which are felt significantly in the short run. We concluded that human development is negatively affected by a sudden decline in the federal government's capital budget expenditure. [ABSTRACT FROM AUTHOR]

We investigate the problem of short-term prediction of the free market price for electricity using various types of forecast models. A transition is made from considering traditional regression and autoregressive models to the proposed combined multivariate models, which also include the time trend and dummy variables. The combined forecast models have been constructed using dedicated statistical software packages. Comparison of the level of accuracy of the predicted values of the market electricity price obtained by different models has not revealed any advantages from the use of combined multivariate regression models. However, the use of the latter permits one to assess the influence of exogenous factors on the predicted variable and can be a fundamental advantage when choosing the forecast model type. [ABSTRACT FROM AUTHOR]

When introducing a new product or service to the market, a manufacturer or retailer is faced with the need to develop a promotion strategy. Taking into account the recent trends in e-commerce and social media marketing, it makes sense to evaluate the possibility of borrowing promotion strategies from producers of abstract, potentially monetized electronic content. The article considers the activity of advertising bots using the example of the social network Facebook. The paper carries out an autocorrelation analysis of the observation results and reveals the frequency of automated requests to the user by advertising bots. The authors construct an autoregressive model of the dynamics of the activity of advertising bots and verbalize Facebook’s strategy to increase traffic to the information space controlled by the social network.

In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods.

The subject of research in the article are algorithms for fast calculation of autoregression coefficients in digital spectral analysis and estimation of the number of arithmetic operations required for their implementation. The aim of the article – comparative analysis of the speed of different algorithms for calculating the coefficients of autoregression as part of the algorithms of spectral analysis, including analysis of the complexity of their microcontroller implementation. Tasks to be solved: selection of spectral analysis methods suitable for diagnostics of technological equipment, analysis of methods for calculating autoregression coefficients and derivation of relations for estimating software complexity of algorithms and calculation of numerical estimates of addition and multiplication for some algorithms, adaptation of developed methods and estimates to microcontrollers. spectrum Applied methods: algorithm theory, Fourier transform, natural series, microcontroller programming. The results obtained: it is shown that spectral estimation methods based on Yul-Walker equations, which require the calculation of autoaggression coefficients, combine sufficient resolution and resistance to interference with acceptable implementation complexity. Estimates of the number of additions and multiplications for the Levinson, Durbin, and Trench algorithms are obtained, and their comparative analysis is performed. The calculation times for microcontroller arithmetic with fixed and floating points were count upon. Conclusions: When constructing spectrum analyzers for the diagnosis of technological equipment, it is advisable to use the Yul-Walker method. A comparison of Levinson, Durbin, and Trench algorithms for calculating autoregression coefficients showed that the Trench method requires a minimum number of additions, and the Durbin method requires a minimum number of multiplications. At microcontroller realization of spectrum analyzers, it is necessary to consider features of the arithmetic used by the controller. The Trench method is the fastest in the case of floating-point arithmetic and small-scale modeling. In other cases, Durbin's method is more effective.

This note proposes time series models for data where the frequency changes over time. As an example, for many countries, in the past, real GDP growth was observed annually and since a few years or decades, the data are available per quarter. Modifying the time series models allows for the incorporation of these older annual data, without a need for imputation. Illustrations for real GDP growth in China and the Netherlands show the merits of the method. [ABSTRACT FROM AUTHOR]

We present a technique to learn parsimonious time-varying autoregressive models from multivariate timeseries. This unsupervised method uncovers interpretable spatiotemporal structure in data via nonsmooth and nonconvex optimization. In each time window, we assume the data follow a linear model parameterized by a system matrix, and we model this stack of potentially different system matrices as a low-rank tensor. Because of its structure, the model is scalable to high-dimensional data and can easily incorporate priors such as smoothness over time. We find the components of the tensor using alternating minimization and prove that any stationary point of this algorithm is a local minimum. We demonstrate on a synthetic example that our method identifies the true rank of a switching linear system in the presence of noise. We illustrate our model's utility and superior scalability over extant methods when applied to several synthetic and real-world examples two types of time-varying linear systems, worm behavior, sea surface temperature, and monkey brain data sets. [ABSTRACT FROM AUTHOR]

Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive model that simultaneously identifies oscillations by their peak frequency, damping time and network structure. We use this decomposition to define a set of Spatio-Spectral Eigenmodes (SSEs) providing a parsimonious description of oscillatory networks. We show that the multivariate system transfer function can be rewritten in these modal coordinates, and that the full transfer function is a linear superposition of all modes in the decomposition. The modal transfer function is a linear summation and therefore allows for single oscillatory signals to be isolated and analysed in terms of their spectral content, spatial distribution and network structure. We validate the method on simulated data and explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide between participant variability in peak frequency and network structure of alpha oscillations and show a distinction between occipital ’high-frequency alpha’ and parietal ’low-frequency alpha’. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person’s behavioural, cognitive or clinical state.

Forecasting the number of Covid-19 cases is a crucial tool in public health policy. In this paper, we construct seasonal autoregressive moving average and autoregressive conditional heteroscedasticity models to forecast the spread of the infection in the UAE. While most of the existing literature is dedicated to forecasting the total number of infections, we endeavor to forecast the number of new infections which is a significantly more challenging task due to the greater volatility. Our models are based on a careful analysis of correlation plots and residual analysis. In addition, we employ highly accurate population data that leads to more reliable outcomes. The results reveal a high degree of accuracy of the proposed forecasting methods. The constructed models can be used by health officials to better anticipate and plan for new cases of Covid-19. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Applicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed. Comparative analysis of received neural networking forecasting model with linear regression has been performed. Conditions, affecting on accuracy and stability of results of the neural network, have been revealed.

The aim of the article is to build a model for predicting behavior of an enterprise under conditions of its interaction with the external environment, involving the formation and support of the optimal trajectory of its behavior. Based on the analysis of research works, the category “enterprise behavior” is considered, and its relationship with the enterprise’s development is determined. There formed rules that constitute its methodological basis for constructing a prognostic model and allow taking into account various aspects of enterprise behavior in the system “enterprise – industry – national economy”. As a result of the research, a system of prediction models, containing the Cobb-Douglas production function, lag, autoregressive and vector autoregressive models is constructed. As part of this model, there investigated the following blocks: production; scientific and technological progress and innovation and marketing component; financial and labor components; type / style of the company in a competitive environment; external interaction. The proposed model for predicting the behavior of an enterprise in an unsteady environment, which has the form of a system of functional dependencies, allows carrying out comprehensive analysis of the enterprise’s internal and external capabilities. The prospect of further research in this area is the development of target guidelines for managing the behavior of an enterprise in a fluctuating market environment, with consideration for its real and potential resources and development opportunities of the industry and the national economy.

We investigated the applicability of ssGBLUP methodology under the autoregressive model (H‐AR) for genomic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle. The genotype data of 1,230 bulls and 1,645 cows were considered in our study. The reproductive traits evaluated were interval from calving to first service (ICF), calving interval (CI) and daughter pregnancy rate (DPR) measured during the first four parities. Reliability and rank correlation were used to compare the H‐AR with the traditional pedigree‐based autoregressive models (A‐AR). In addition, a validation study was performed considering different scenarios. Higher genomic estimated breeding values (GEBV) reliabilities were obtained for genotyped bulls when evaluated under the H‐AR model, with emphasis on bulls with less than 9 daughters. For this group, the averages of GEBV reliabilities corresponded to 0.62, 0.69 and 0.62 for ICF, CI and DPR, respectively, while the averages obtained by the A‐AR model were 0.27, 0.15 and 0.16. The validation study was favourable to H‐AR. The best results were observed in the scenario where genotyped cows were combined with contributing bulls (genotyped bulls with daughter or relationship information in the population). Overall, the results suggest that ssGBLUP methodology under the autoregressive model is a feasible and applicable approach to be used in genomic analyses of longitudinal reproductive traits in Portuguese Holstein cattle. [ABSTRACT FROM AUTHOR]

We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations, and leveraging similarities can improve the estimation of effects. We build on extensive literature and develop an autoregressive model of order one that models haplotype effects by leveraging phylogenetic relationships described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network, and we refer to the model as the haplotype network model. The model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. Our key contribution is that we obtain a sparse model, and by using hierarchical autoregression, the flow of information between similar haplotypes is estimated from the data. A simulation study shows that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially with few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a study of mitochondrial haplotype effects on milk yield in cattle. We provide R code to fit the model with the INLA package.