Search

Each states play pivotal roles in contributing to the economic performance of the nation. They have their own unique economic strengths, resources, and industries that contribute to the overall economic development. Terengganu, an east coast state in Malaysia has historically faced economic challenges, marked by a lag in development compared to other regions. The fundamental problem addressed is the potential correlation between educational attainment and the region’s economic growth, aiming to understand how advancements in education might serve as a catalyst for economic progress. The objective of this research is to empirically analyze the impact of educational achievement on the economic development in Terengganu. Utilizing secondary data sourced from the Department of Statistics Malaysia and Economy Planning Unit Terengganu spanning from 2006 to 2022, Autoregressive Distributed Lag (ARDL) model has been employed to estimate and understand the relationship between educational attainment and economic development. The findings suggested that educational achievement plays positive impact in heightening the economic development within the region. Education contributes to the development of human capital, fostering a skilled and knowledgeable populace capable of engaging in diverse economic activities. This, in turn, attracts investments and facilitates the emergence of industries and sectors that rely on a knowledgeable and skilled labour force. This study would provide valuable insights for policymakers and academicians to formulate targeted policies and initiatives, ultimately enhancing the economic development of the states, thus contribute to a prosperous Malaysia.

IntroductionFunctional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies.MethodsIn this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named “IDAR” (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets.ResultsConventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively.DiscussionThis study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power.

Companies are trying to harness the potential of the current stage of development of the digital industry and make it cost-effective, which requires not only technological innovation but also the transformation of many existing business models. Based on an analysis of examples of digital subscription companies, this article provides additional explanations, including definitions and constituent characteristics of digital subscription business models. The aim of the article was to identify trends in the development of digital subscription services by analysing the number of active subscribers and the impact of COVID-19 on this indicator. Autoregression and analysis methods were used in the study, and the implementation of the selected models was carried out using the Python-3 programming language. The article describes a theoretical basis that determine the feasibility of using the subscription model for the company under non-deterministic critical conditions. The main approaches to forecasting the company’s performance indicators are identified. The type of data to be forecasted is determined, and the necessity of using autoregressive models for further analysis is declared. Factors associated with the COVID-2019 incidence that should be considered as exogenous variables were identified. The multicriteria selection problem was constructed by restricting the family of autoregressive models and determining the criteria for forecasting and data preparation time, forecast accuracy, and the possibility of considering external factors in determining the algorithm efficiency. It is found that moving average autoregression is the most effective. Given the further application of this algorithm to predict the number of subscribers, the expediency of implementing a subscription strategy for the company under non-deterministic critical conditions is declared. It is possible to effectively apply the selected mathematical model to predict the performance of a subscription company. The practical significance of the work lies in the possibility of creating certain benchmarks for the development of the market and a business strategy for product promotion, which will be based on an understanding of the potential benefits and threats of the market for digital subscription products and services

The assessment of the effect of the informal use of a foreign currency on the corresponding FX rates toward the national currency is a very difficult task, requiring direct calculation and modeling. It comes mostly because of the unknown quantity of foreign money used in the informal sector, but also because of the lack of quantitative calculation models. To overcome this gap and to realize a qualitative description of this effect in a concrete economic environment, we propose herein a comparative analysis between the behavior of two typical FX rate series recorded in the Albanian currency market, the Euro-ALL and USD-ALL, providing that the Euro is used commonly as a national currency substitute in the informal economy and the USD is not. So, we have evidenced that the un-stationarity degree of the Bid and Ask spread distribution for the Euro-ALL FX series is higher than for the corresponding USD-ALL case, but with a lower variance. Those features occurring simultaneously can be explained by assuming that informal use of the Euro acts as an additional perturbation on the FX system, imposing high nonstationary, but at the same time it provides reservoir or source features for the money disbalances, reducing the average fluctuations. Next, the depth of the market measured by the average Bid-Ask Spread has resulted in a smaller price for the Euro currency, indicating a lower cost of the transactions and reinforcing the assumption regarding the distribution’s in-stationarity features. Based on those indicatory findings, we propose to realize indirect evidence for our assumptions by comparing the reproduction of the corresponding distribution using autoregressive models. In this case, we have evidenced that the distribution of the FX Euro-ALL spread can be reproduced better if we include in standard ARCHX (m, n, p) models a term that mimics the informality measure. When applying the same procedure for the USD-ALL spread, the resulting distribution has not matched equally well with the original ones. Those findings have been discussed in the framework of an alternative description of the effect of the informal use of foreign currency in a small economy with a sizeable informal sector, which convene our current system under analysis, but we believe that they can be applicable for similar economic environments. Doi: 10.28991/HEF-2024-05-01-02 Full Text: PDF

Generating dance movements from music has been considered a highly challenging task, as it requires the model to comprehend concepts from two different modalities: audio and video. However, recently, research on dance generation based on deep learning has been actively conducted. Existing dance generation researches tend to focus on generating dances in limited genres or for single dancer, so when K-pop music that mixes multiple genres was applied to existing methods, they failed to generate dances of various genres or group dances. In this paper, we propose the K-pop dance generation model in an autoregressive manner, a system designed to generate two-person synchronous dances based on K-pop music. To achieve this, we created a dataset by collecting videos of multiple dancers simultaneously dancing to K-pop music and dancing in various genres. Generating synchronous dances has two meanings: one is to generate a dance that goes well with the input music and dance when both are given, and the other is to simultaneously generate multiple dances that match the given music. We call them secondary dance generation and group dance generation, respectively, and designed the proposed model, which can perform both two generation methods. In addition, we would like to propose additional learning methods to make a model that better generates synchronous dances. To assess the performance of the proposed model, both qualitative and quantitative evaluations are conducted, proving the effectiveness and suitability of the proposed model when generating synchronous dances for K-pop music.

This study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function. Traditional approaches in this area predict the future behavior of dynamical systems by inferring the evolution function. However, these methods may confront obstacles due to the presence of missing variables, which are usually attributed to challenges in measurement and a partial understanding of the system of interest. To overcome this obstacle, we introduce the autoregressive with slack time series (ARS) model, that simultaneously estimates the evolution function and imputes missing variables as a slack time series. Assuming time-invariance and linearity in the (underlying) entire dynamical time series, our experiments demonstrate the ARS model’s capability to forecast future time series. From a theoretical perspective, we prove that a 2-dimensional time-invariant and linear system can be reconstructed by utilizing observations from a single, partially observed dimension of the system.

This study focuses on real-time model predictive control (MPC) implementation for a point-absorber-type wave energy converter (WEC) to achieve maximum wave power extraction. To address the MPC dependence on current and future wave excitation force knowledge, the control framework includes a square-root unscented Kalman filter (SR-UKF) to estimate the wave excitation force, whereas an autoregressive (AR) model forecasts the excitation force over the prediction horizon. The MPC utilizes a linear model of the point absorber, and its cost function is formulated as a quadratic programming (QP) problem with the objective of maximizing the wave energy extraction subject to physical limitations and constraints. A set of Laguerre functions was used to reduce the MPC computational complexity by decreasing the number of decision variables in the QP problem. The MATLAB/Simulink simulation results showed that the embodiment of point absorber nonlinearities by the SR-UKF enhanced the estimation accuracy compared with that of linear Kalman filters in terms of maximum error, root mean square error, and goodness of fit. Moreover, an AR model of order 100 provided satisfactory prediction performance for a 4 s prediction. The MPC strategy was simulated to evaluate its efficiency with respect to the absorbed wave power and generated electrical power under different sea-state conditions and disturbance uncertainties. Depending on sea state, it was found that the proposed MPC strategy generated 27.8% to 80% more electrical power than resistive loading control. Finally, a hardware-in-the-loop experimental setup was used to validate the computational viability of the proposed control framework in real-time WEC applications.

Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain–computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.

This paper analyzes the current situation of university English teaching and constructs a trinity of EGP+ESP+EIC multiple teaching innovation systems for university English based on the principles of English teaching innovation. In order to analyze this innovative teaching system of English, a stratified statistical model and a spatial autoregressive model are combined, and four ordinal stratified spatial autoregressive Probit models are innovatively constructed to model data where the dependent variable is ordinal, and there is a spatial dependency situation with a stratified structure. The analysis shows that in the process of continuous innovation of English teaching mode, the number of people who are dissatisfied with English teaching decreases by 30%, the number of people who are very satisfied increases by 35%, and the number of people who are very satisfied accounts for 42.5% of the total number of people and 90% of the total number of people who are satisfied in the final stage of innovation of English teaching in university. The English teaching innovation strategy for university students based on this study is feasible.

This paper proposes a novel censored autoregressive conditional Fréchet (CAcF) model with a flexible evolution scheme for the time-varying parameters, which allows deciphering tail risk dynamics constrained by price limits from the viewpoints of different risk preferences. The proposed model can well accommodate many important empirical characteristics of financial data, such as heavy-tailedness, volatility clustering, extreme event clustering, and price limits. We then investigate tail risk dynamics via the CAcF model in the price-limited stock markets, taking entropic value at risk (EVaR) as a risk measurement. Our findings suggest that tail risk will be seriously underestimated in price-limited stock markets when the censored property of limit prices is ignored. Additionally, the evidence from the Chinese Taiwan stock market shows that widening price limits would lead to a decrease in the incidence of extreme events (hitting limit-down) but a significant increase in tail risk. Moreover, we find that investors with different risk preferences may make opposing decisions about an extreme event. In summary, the empirical results reveal the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited stock markets, providing a new tool for financial risk management.

The time-inhomogeneous autoregressive model AR(1) is studied, which is the process of the form ${X_{n+1}}={\alpha _{n}}{X_{n}}+{\varepsilon _{n}}$, where ${\alpha _{n}}$ are constants, and ${\varepsilon _{n}}$ are independent random variables. Conditions on ${\alpha _{n}}$ and distributions of ${\varepsilon _{n}}$ are established that guarantee the geometric recurrence of the process. This result is applied to estimate the stability of n-steps transition probabilities for two autoregressive processes ${X^{(1)}}$ and ${X^{(2)}}$ assuming that both ${\alpha _{n}^{(i)}}$, $i\in \{1,2\}$, and distributions of ${\varepsilon _{n}^{(i)}}$, $i\in \{1,2\}$, are close enough.

Ensuring the reliability of data obtained from Wireless Sensor Networks (WSNs) deployed in farmland to monitor soil parameters is crucial for optimizing smart irrigation. These distributed sensor nodes encounter various challenges and are vulnerable to sensor faults that can significantly degrade the network's service quality. In this article, we present an innovative approach for detecting sensor faults within WSNs for smart irrigation by combining an autoregressive model with a Kalman filter. Integrating the Kalman filter and the autoregressive model combines their strengths in a synergistic manner. The algorithm is developed with consideration for the resource constraints of the sensor nodes and addresses the challenge of lacking ground truth information for the monitored area. The primary advantage of this proposed technique lies in its simplicity of implementation, requiring minimal computational complexity while enhancing the application's reliability. Through experimentation and validation, we demonstrate the effectiveness of this combined approach in detecting sensor fault detection in real-world WSNs scenarios.

This study presents alpha-stable autoregressive (AR) modeling of the dynamics of Chua's circuit in the presence of heavy-tailed noise. The parameters of the AR time series are estimated using the covariation-based Yule-Walker method, and the parameters of alpha-stable distributed residuals are calculated using the regression type method. Visual depictions of the calculated parameters of the AR model and alpha-stable distributions of residuals are presented. The medians of the estimated parameters of the AR model and alpha-stable distributions parameters of residuals are presented for heavy-tailed noise with various stability index parameters. Thus, the impulsive behavior of Chua's circuit can be modeled as alpha-stable AR time series, and the model can provide an alternative approach to describe the chaotic systems driven by heavy-tailed noise.

Lossless image compression is an important research field in image compression. Recently, learning-based lossless image compression methods achieved impressive performance compared with traditional lossless methods, such as WebP, JPEG2000, and FLIF. The aim of the lossless image compression algorithms is to use shorter codelength to represent images. To encode an image with fewer bytes, eliminating the redundancies among the pixels in the image is highly important. Hence, in this paper, we explore the idea of combining an autoregressive model for the raw images based on the end-to-end lossless architecture proposed to enhance the performance. Furthermore, inspired by the successful achievements of Channel-conditioning models, we propose a Multivariant Mixture distribution Channel-conditioning model (MMCC) in our network architecture to boost performance. The experimental results show that our approach outperforms most classical lossless compression methods and existing learning-based lossless methods.

This work proposes a transformer-based model capable of generating music in its symbolic domain, in a controllable fashion. The ultimate goal of this is to build a system with which people can compose music collaboratively with a computer. Using an NLP model as a base (GPT-2), we take advantage of the similarities across symbolic music representation and written language to build a model capable of conditionally predicting musical sequences. Controllability is achieved without explicit programming for it, and does not require extensive retraining of the model. A study with 939 participants was performed to evaluate this controllability. The results of this suggest the proposed method is indeed effective and can be used to control the generation of music in its symbolic domain. The method itself is flexible to any desired “control”, but this work focuses specifically on the emotion conveyed when one listens to a piece of music.

Lag signal problems occur in acquiring X-ray image sequences from dynamic flat-panel detectors due to amorphous pixel photodiodes and incomplete readouts. Based on a linear, time-invariant system with a multiple exponential moving average model for the lag signal, Hsieh et al. proposed a recursive deconvolution algorithm for lag corrections, and Starman et al. proposed a nonlinear correction algorithm to cope with nonlinear lag properties. In this paper, we consider an autoregressive model of order 1 to describe lag signals and conduct lag corrections through simple linear and nonlinear decorrelation schemes for the exposure-dependent lag signals. In order to correct the current image frame, using only the previous frame is enough for the autoregressive model. We also evaluate the lag correction performance of the proposed lag correction algorithms by measuring the lag correction factor to show the successful removal of the lag signals with low computational complexities.

We present a new set of equations for tree level volume and aboveground biomass estimation for ocote pine (Pinus oocarpa Schiede ex Schltdl). These equation systems are the first developed for this species in Nicaragua. The first system includes a taper function, a merchantable volume equation, and volume equations for stem, coarse branches, and whole trees. The second system estimates whole tree and individual tree component biomass (stem wood, bark, branches, and needles). Data from 112 sampled trees were used for models’ development. Seemingly Unrelated Iterative Regression and the Generalized Method of Moments were used to simultaneously fit the volume and biomass equations systems, respectively; both methods ensure additivity and compatibility between equations. Weighted regression and a second-order continuous autoregressive error structure were used to correct heteroscedasticity and autocorrelation within the hierarchical dataset. The predictive power of the new proposed equations is higher than the currently used models for P. oocarpa in the country. These equation systems represent a scientific advancement that will enhance forest inventories, optimize timber management of the species, and facilitate accurate monitoring of forest carbon dynamics. Additionally, the new equations will contribute to a more precise accounting of CO2 emissions from the country’s forestry sector.

В работе представлены результаты моделирования и анализа параметров ионосферы в периоды магнитных бурь 2017-2021 гг. Использовались данные кри- тической частоты F2-слоя ионосферы (foF2 ) (по данным ионозонда ст. , п-ов Камчатка, ИКИР ДВО РАН). Моделирование выполнялось на основе разработанной авторами обобщенной многокомпонентной модели параметров ионосферы (ОМКМ). Представленная в работе ОМКМ позволяет детально изучать динамику параметров ионосферы в возмущенные периоды. Идентификация модели основана на совместном применении вейвлет преобразования и авторегрессионных моделей (АРПСС модели). ОМКМ описывает три класса аномалий, характеризующих сильные (класс 3), умеренные (класс 2) и слабые (класс 1) ионосферные возмущения. Исследование динамики параметров ионосферы проводилось в зависимости от силы геомагнитного возмущения (рассматривались события слабой, умеренной и высокой интенсивности). В процессе моделирования обнаружены ионосферные аномалии разной интенсивности и продолжительности. Накануне умеренных и сильных магнитных бурь отмечен факт высокой частоты эффекта предповышения в ионосфере, имеющий важную прикладную значимость.

Abstract In this paper, we propose a general framework for the reconstruction of the underlying cross-regional transmission network contributing to the spread of an infectious disease. We employ an autoregressive model that allows to decompose the mean number of infections into three components that describe: intra-locality infections, inter-locality infections, and infections from other sources such as travelers arriving to a country from abroad. This model is commonly used in the identification of spatiotemporal patterns in seasonal infectious diseases and thus in forecasting infection counts. However, our contribution lies in identifying the inter-locality term as a time-evolving network, and rather than using the model for forecasting, we focus on the network properties without any assumption on seasonality or recurrence of the disease. The topology of the network is then studied to get insight into the disease dynamics. Building on this, and particularly on the centrality of the nodes of the identified network, a strategy for intervention and disease control is devised.

We present an any-to-one voice conversion (VC) system, using an autoregressive model and LPCNet vocoder, aimed at enhancing the converted speech in terms of naturalness, intelligibility, and speaker similarity. As the name implies, non-parallel any-to-one voice conversion does not require paired source and target speeches and can be employed for arbitrary speech conversion tasks. Recent advancements in neural-based vocoders, such as WaveNet, have improved the efficiency of speech synthesis. However, in practice, we find that the trajectory of some generated waveforms is not consistently smooth, leading to occasional voice errors. To address this issue, we propose to use an autoregressive (AR) conversion model along with the high-fidelity LPCNet vocoder. This combination not only solves the problems of waveform fluidity but also produces more natural and clear speech, with the added capability of real-time speech generation. To precisely represent the linguistic content of a given utterance, we use speaker-independent PPG features (SI-PPG) computed from an automatic speech recognition (ASR) model trained on a multi-speaker corpus. Next, a conversion model maps the SI-PPG to the acoustic representations used as input features for the LPCNet. The proposed autoregressive structure enables our system to produce the following prediction step outputs from the acoustic features predicted in the previous step. We evaluate the effectiveness of our system by performing any-to-one conversion pairs between native English speakers. Experimental results show that the proposed method outperforms state-of-the-art systems, producing higher speech quality and greater speaker similarity.

This paper develops the empirical likelihood ( $ \mathrm {EL} $ ) inference procedure for parameters in autoregressive models with the error variances scaled by an unknown nonparametric time-varying function. Compared with existing methods based on non-parametric and semi-parametric estimation, the proposed test statistic avoids estimating the variance function, while maintaining the asymptotic chi-square distribution under the null. Simulation studies demonstrate that the proposed $ \mathrm {EL} $ procedure (a) is more stable, i.e., depending less on the change points in the error variances, and (b) gets closer to the desired confidence level, than the traditional test statistic.

Abstract In lossless audio compression, the predictive residuals must remain sparse when entropy coding is applied. The sign algorithm (SA) is a conventional method for minimizing the magnitudes of residuals; however, this approach yields poor convergence performance compared with the least mean square algorithm. To overcome this convergence performance degradation, we propose novel adaptive algorithms based on a natural gradient: the natural-gradient sign algorithm (NGSA) and normalized NGSA. We also propose an efficient natural-gradient update method based on the AR(p) model, which requires O ( p ) $\mathcal {O}(p)$ multiply–add operations at every adaptation step. In experiments conducted using toy and real music data, the proposed algorithms achieve superior convergence performance to the SA. Furthermore, we propose a novel lossless audio codec based on the NGSA, called the natural-gradient autoregressive unlossy audio compressor (NARU), which is open-source and implemented in C. In a comparative experiment with existing, well-known codecs, NARU exhibits superior compression performance. These results suggest that the proposed methods are appropriate for practical applications.

Forecasting of vegetable area, production, and productivity of Nepal was made from the historical data of 1977/78 to 2019/20 by using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models. The best fitted ARIMA models were chosen based on the minimum value of the selection criterion, Akaike information criteria (AIC) and Bayesian information criteria (BIC). ARIMA (0, 2, 1) model was found suitable for all areas and production, whereas ARIMA (0, 2, 0) model was best fitted for forecasting vegetable productivity. The model was cross-validated by comparing the point prediction with the actual test series data from 2015/16 to 2019/20. The performances of models were determined from the mean absolute percent error (MAPE) value. The MAPE was found to be 2.70%, 2.40%, and 3.80%, respectively for the prediction of area, production, and productivity. The forecast was made for the immediate five years (2020/21 to 2024/25), and it showed an increasing value for area and production while the forecasts of productivity had lower values. The vegetable production policy in Nepal should be planned following accurate forecasts to increase production in the upcoming years. Awareness among the vegetable growers should be raised in the following years with appropriate extension programs.

There are different flow prediction models such as Autoregressive models, Autoregressive moving average models, first-order autoregressive-moving average models, etc. The main purposes of this dissertation were to fit a model to represent a river flow data of 10 rivers in the Northern part of Cyprus. The modeling was built on the estimate of parameters, modeling the residuals, generating synthetic river flows, and checking for the goodness of fit to the monitored data. Finally, the findings were used to evaluate the synthetic series for future flow predictions. The study on available data demonstrated that the (AR) model was an efficient and reliable technique in which, the model identification technique was supplemented by the Akaike's information criterion (AIC) in order to decide the type and the order of the model. The Box-Pierce Porte Manteau test is used to check the dependency of residuals. it is recommended to generate stochastic modeling for the downstream drainage areas of the 10 rivers in which the surface geology totally changes and surface flow turns to be a subsurface flow due to the gravel and pebbles distributed all around the riverbeds.

Energy disaggregation refers to the process of obtaining the energy consumption of several appliances in a house by disaggregating the aggregate power consumption measured by an electrical meter. Currently, deep learning methods are widely applied in this field. Real-time energy disaggregation is an important branch of energy disaggregation. Based on the Short Sequence-to-Point (Short Seq2point) (Odysseas) network structure, a real-time energy disaggregation algorithm based on multi-channels deep convolutional neural networks (MC-DCNN) and autoregressive model (AR) is proposed in this paper, which obtains theenergy consumption of appliances at the current time point by disaggregating the historical aggregate power consumption to achieve delivering disaggregation results in real-time. The proposed method takes the original aggregate power sequence and differential power signal as the input of the network, and extracts the information of different time lengths in the sequence using multi-channels deep convolutional neural networks with a modified concatenate layer, so that the network can adapt to different appliances with different operating modes. In addition, the traditional autoregressive model is added as the linear component for solving the problem that the scale of the output is insensitive to the scale of the input in the neural network model. Finally, the proposed method was tested on the UK-DALE and REDD datasets, and the experimental results show that the method has good disaggregation performance on both datasets, has a small number of parameters and achieves fast inference.

Automatically synthesizing dance motion sequences is an increasingly popular research task in the broader field of human motion analysis. Recent approaches have mostly used recurrent neural networks (RNNs), which are known to suffer from prediction error accumulation, usually limiting models to synthesize short choreographies of less than 100 poses. In this paper we present a multimodal convolutional autoencoder that combines 2D skeletal and audio information by employing an attention-based feature fusion mechanism, capable of generating novel dance motion sequences of arbitrary length. We first validate the ability of our system to capture the temporal context of dancing in a unimodal setting, by considering only skeletal features as input. According to 1440 rating answers provided by 24 participants in our initial user-study, we show that the optimal performance was presented by the model that was trained with input sequences of 500 poses. Based on this outcome, we train the proposed multimodal architecture with two different approaches, namely teacher-forcing and self-supervised curriculum learning, to deal with the autoregressive error accumulation phenomenon. In our evaluation campaign, we generate 1800 sequences and compare our method against two state-of-the-art approaches. Through qualitative and quantitative experiments we demonstrate the improvements introduced by the proposed multimodal architecture in terms of realism, motion diversity and multimodality, reducing the Fréchet Inception Distance (FID) metric value by 0.39. Subjective results confirm the effectiveness of our approach to synthesize diverse dance motion sequences, reporting a 6% increase in style consistency preference according to 1800 answers provided by 45 evaluators.

Damage identification plays an important role in enhancing resilience by facilitating precise detection and assessment of structural impairments, thereby strengthening the resilience of critical infrastructure. A current challenge of vibration-based damage detection methods is the difficulty of enhancing the precision of the detection results. This problem can be approached through improving the noise reduction performance of algorithms. A novel method based partially on the errors-in-variables (EIV) model and its total least-squares (LS) algorithm is proposed in this study. Compared with a classical damage detection approach involving adoption of auto-regressive (AR) models and the least-squares (LS) method, the proposed method accounts for all the observation errors as well as the relationships between them, especially in an elevated level of noise, which leads to a better accuracy. Accordingly, a shaking table test and its corresponding finite element simulation of a full-scale web steel structure were conducted. The acceleration time-series output data of the model after suffering from different seismic intensities were used to identify damage using the presented detection method. The response and identification results of the experiment and the finite element analysis are consistent. The finding of this paper indicated that the presented approach is capable of detecting damage with a higher accuracy, especially when the signal noise is high.

The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core component of the autoregressive model. Therefore, short-term effects can be modeled in an easy way, but the global structure of the model is not obvious. However, this global structure is a crucial aid in the model selection process in data analysis. If the global properties are not reflected in the data, a corresponding model is not compatible. This helpful knowledge avoids unsuccessful modeling attempts. This article analyzes the global structure of the autoregressive model through the derivation of a closed form. In detail, the closed form of an autoregressive model consists of the basis functions of a fundamental system of an ordinary differential equation with constant coefficients; i.e., it consists of a combination of polynomial factors with sinusoidal, cosinusoidal, and exponential functions. This new insight supports the model selection process.

Objective: The aim of the article was to forecast the necessary pace of changes in the share of RES in the V4 countries resulting from the EU’s renewable energy sources directive compared to other European Union countries. Research Design & Methods: The research area included all EU Member States, and in particular the Visegrad Group countries. Forecasts of future RES share values were based on two models: Holt-Winters and the autoregressive (AR) model based on EUROSTAT statistical data. Findings: The potential failure to meet the recommendations of the RES share in gross final energy consumption for 2022 concerns 19 of the 27 Member States, of which 2 countries belong to the Visegrad Group. Implications & Recommendations: The research has implications mainly to raise awareness of the direction of RES development in the European Union countries. Contribution & Value Added: The study contributes to the estimation of the future value of the share of renewable energy sources in the V4 countries compared to other countries European Union on the basis of the current activities of these Member States. The forecast makes it possible to initially determine the possibility of meeting the specific target regarding the share of renewable energy sources in the final energy consumption set out in the European Union directive. Article type: research article

In order to accelerate the development of new energy industry and social economy, this paper presents an empirical analysis of the role of new energy transformation in promoting China’s economy. On the basis of analyzing the concept and types of new energy, the necessity of transformation and upgrading of new energy industry is discussed. The new energy consumption data from 2010 to 2020 and China’s GDP data are selected as the basic data, and the MS-VAR model is used as the base model for the empirical analysis. The model combines the Markov zone transition model and the autoregressive model, which is suitable for analyzing non-linear problems. The results of the empirical analysis show that the new energy transition is an important way to promote new energy consumption, and it plays a role in promoting the balanced development of China’s economic growth. Combining the results of the empirical analysis, this paper gives suggestions related to the new energy transition from the institutional, economic and technological perspectives.

The analysis of longitudinal survey data is often complicated when informative sampling or nonignorable missing data exists. Existing methods that can handle both informative sampling and nonignorable missing data are only limited to the situation of no time dependence in the data. In this paper, we develop a sample likelihood based approach for estimation of time series model in longitudinal survey data under informative sampling and nonignorable missingness. In particular, some informative sampling models and a response model are proposed to describe the mechanisms of informative sampling and nonignorable missingness. A sample likelihood is derived based on the conditional distribution of the observed measurements. Also, an effective computation algorithm is developed to compute the sample likelihood. Simulation studies are carried out to investigate the performance of the proposed estimator. A real data example based on data from AIDS Clinical Trial Group 193A Study is presented to illustrate the proposed method.

Multivariate time-series forecasting derives key seasonality from past patterns to predict future time-series. Multi-step forecasting is crucial in the industrial sector because a continuous perspective leads to more effective decisions. However, because it depends on previous prediction values, multi-step forecasting is highly unstable. To mitigate this problem, we introduce a novel model, named stacked dual attention neural network (StackDA), based on an encoder-decoder. In dual attention, the initial attention is for the time dependency between the encoder and decoder, and the second attention is for the time dependency in the decoder time steps. We stack dual attention to stabilize the long-term dependency and multi-step forecasting problem. We add an autoregression component to resolve the lack of linear properties because our method is based on a nonlinear neural network model. Unlike the conventional autoregressive model, we propose skip autoregressive to deal with multiple seasonalities. Furthermore, we propose a denoising training method to take advantage of both the teacher forcing and without teacher forcing methods. We adopt multi-head fully connected layers for the variable-specific modeling owing to our multivariate time-series data. We add positional encoding to provide the model with time information to recognize seasonality more accurately. We compare our model performance with that of machine learning and deep learning models to verify our approach. Finally, we conduct various experiments, including an ablation study, a seasonality determination test, and a stack attention test, to demonstrate the performance of StackDA.

With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended to be informative, a large portion of it necessitates the use of text mining and sentiment analysis models. It is a matter of concern that reviews on social media lack text context semantics. A model for sentiment classification for customer reviews based on manifold dimensions and manifold modeling is presented to fully exploit the sentiment data provided in reviews and handle the issue of the absence of text context semantics. This paper uses a deep learning framework to model review texts using two dimensions of language texts and ideogrammatic icons and three levels of documents, sentences, and words for a text context semantic analysis review that enhances the precision of the sentiment categorization process. Observations from the experiments show that the proposed model outperforms the current sentiment categorization techniques by more than 8.86%, with an average accuracy rate of 97.30%.

The rolling bearing is an important part of mechanical equipment, and its performance significantly affects the quality and life of the mechanical equipment. This article uses the integrated fiber Bragg grating resonant structure sensor excited by periodic micro-shocks caused by micro faults to realize the extraction of information relating to potential faults. Because the fault signal is weak and can easily be interfered with by ambient noise, in order to extract the effective signal, this article determines the autoregressive model of bearing vibration by the final prediction error criterion and the recursive least squares estimation algorithm. The augmented state space model is established based on the autoregressive model. A Kalman filter is used to reduce the noise interference, and then the reduction noisy signal is analyzed by power spectrum and improved autocorrelation envelope spectrum to realize the detection of bearing faults. Through data analysis and method comparison, the proposed improved autocorrelation envelope spectrum analysis can directly extract the bearing fault frequency, which is superior to other methods such as cepstral analysis.

This paper presents an overview of the main methods used to identify autoregressive models with additive noises. The classification of identification methods is given. For each group of methods, advantages and disadvantages are indicated. The article presents the simulation results of a large number of the described methods and gives recommendations on choosing the best methods.

In a time series context, the study of the partial autocorrelation function (PACF) is helpful for model identification. Especially in the case of autoregressive (AR) models, it is widely used for order selection. During the last decades, the use of AR-type count processes, i.e., which also fulfil the Yule–Walker equations and thus provide the same PACF characterization as AR models, increased a lot. This motivates the use of the PACF test also for such count processes. By computing the sample PACF based on the raw data or the Pearson residuals, respectively, findings are usually evaluated based on well-known asymptotic results. However, the conditions for these asymptotics are generally not fulfilled for AR-type count processes, which deteriorates the performance of the PACF test in such cases. Thus, we present different implementations of the PACF test for AR-type count processes, which rely on several bootstrap schemes for count times series. We compare them in simulations with the asymptotic results, and we illustrate them with an application to a real-world data example.

In this paper, we investigate the impact of mobility on the spread of COVID-19 in Tehran, Iran. We have performed a time series analysis between the indicators of public transit use and inter-city trips on the number of infected people. Our results showed a significant relationship between the number of infected people and mobility variables with both short-term and long-term lags. The long-term effect of mobility showed to have a consistent lag correlation with the weekly number of new COVID-19 positive cases. In our statistical analysis, we also investigated key non-transportation variables. For instance, the mandatory use of masks in public transit resulted in observing a 10% decrease in the number of infected people. In addition, the results confirmed that super-spreading events had significant increases in the number of positive cases. We have also assessed the impact of major events and holidays throughout the study period and analyzed the impacts of mobility patterns in those situations. Our analysis shows that holidays without inter-city travel bans have been associated with a 27% increase in the number of weekly positive cases. As such, while holidays decrease transit usage, it can overall negatively affect spread control if proper control measures are not put in place. The result and discussions in this paper can help authorities understand the effects of different strategies and protocols with a pandemic control and choose the most beneficial ones.

A sentinel network, Obépine, has been designed to monitor SARS-CoV-2 viral load in wastewaters arriving at wastewater treatment plants (WWTPs) in France as an indirect macro-epidemiological parameter. The sources of uncertainty in such a monitoring system are numerous, and the concentration measurements it provides are left-censored and contain outliers, which biases the results of usual smoothing methods. Hence, the need for an adapted pre-processing in order to evaluate the real daily amount of viruses arriving at each WWTP. We propose a method based on an auto-regressive model adapted to censored data with outliers. Inference and prediction are produced via a discretized smoother which makes it a very flexible tool. This method is both validated on simulations and real data from Obépine. The resulting smoothed signal shows a good correlation with other epidemiological indicators and is currently used by Obépine to provide an estimate of virus circulation over the watersheds corresponding to about 200 WWTPs.

With energy serious shortage of the Nigerian Power Sector owing to industry deregulation, abrupt variations in electricity demand, and increasing population density, Nigeria's economic development has been restricted. Thus, it is significant to balance the relationship between power generation and consumption, and further stabilize the two in a reasonable scope. To achieve balance, an accurate model to fit and predict electricity generation and consumption in Nigeria is required. This study, therefore, proposes a comparative study on stochastic modeling; (Harvey model, Autoregressive model, and Markov chain model) for forecasting electricity generation and consumption in Nigeria. The data gathered were analyzed and the model parameters were estimated using the maximum likelihood estimation technique. The comparative performance revealed that the Markov chain model best-predicted electricity generation than the Harvey and Autoregressive models. Also, for electricity consumption, results showed that the Harvey model predicted best than the Markov and Autoregressive models for electricity consumption. Thus, the Markov and Harvey model used to forecast electricity generation and consumption in Nigeria for the next 20 years (2018 to 2037) did not only reveal that electricity generation and consumption will continue to increase from 3,692.11 mln kW/h to 18,250.67 mln kW/h and from 2,961.10 mln kW/h to 127,071.30 mln kW/h respectively but also indicates high accuracy and the reference value of these models.

In this article, we presented a novel spectral estimation method, the dyadic aggregated autoregressive model (DASAR), that characterizes the spectrum dynamics of a modulated signal. DASAR enhances automatic modulation classification (AMC) on environments where new or unknown modulation techniques are introduced, and only size-restricted data is accessible to train classification algorithms. A key component for obtaining efficient machine learning-based classification is the development of valuable knowledge-descriptive features. DASAR constructs a multi-level spectral representation by subdividing a signal into successive dyadic segments where each partition is modeled as an aggregation of single-frequency autoregressive processes. Thus, the model ensures a robust representation at the segment level, while the multi-level decomposition can capture time-varying spectra. As a feature extraction model, DASAR can provide useful learning features related to signals with complex spectra. The effectiveness of our model was tested on a dataset comprised of 11 different modulation techniques and realistic transmission medium characteristics. Using only 200 128-point samples per modulation scheme (1% of the available signal samples) and a proper selection of a classification algorithm, DASAR reaches accuracy up to 70.96% compared with a maximum accuracy of 43.62% using the state-of-art methods tested under the same conditions.

Abstract Real magnetotelluric (MT) data errors are commonly correlated, but MT inversions routinely neglect such correlations without an investigation on the impact of this simplification. This paper applies a hierarchical trans-dimensional (trans-D) Bayesian inversion to examine the effect of correlated MT data errors on the inversion for subsurface geoelectrical structures, and the model parameterization (the number of conductivity interfaces) is treated as an unknown. In the inversion considering error correlations, the data errors are parameterized by the first-order autoregressive (AR(1)) process, which is included as an unknown in the inversion. The data information itself determines the AR(1) parameter. The trans-D inversion applies the reversible-jump Markov chain Monte Carlo algorithm to sample the trans-D posterior probability density (PPD) for the model parameters, model parameterization and AR(1) parameters, accounting for the uncertainties of the model dimension and data error correlation in the uncertainty estimates of the conductivity profile. In the inversion ignoring the correlation, we neglect the correlation effect by turning off the AR(1) parameter. Then the correlation effect on the MT inversion can be examined upon comparing the posterior marginal conductivity profiles from the two inversions. Further investigation is then carried out for a synthetic case and a real MT data example. The results indicate that for strong correlation cases, neglecting error correlations can significantly affect the inversion results.

The Weibull distribution is one of the widely known lifetime distribution that has been extensively used for modelling data in reliability and survival analysis. A generalization of both the Marshall-OlkinWeibull distribution and the Weibull truncated negative binomial distribution is introduced and studied in this article. Various distributional properties of the new distribution are derived. Estimation of model parameters using the method of maximum likelihood is discussed. Applications to a real data set is provided to show the flexibility and potentiality of the new distribution over other Weibull models. The first order autoregressive minification process with the new distribution as marginal is also developed. We hope that the new model will serve as a good alternative to other models available in the literature for modeling positive real data in several areas.

Credit is an economic category and is also a product of the commodity economy, which exists through many socio-economic forms to promote economic growth. Therefore, the objective of this paper is to analyst, compare and forecast domestic credit growth in Vietnam's and China's economy. Thus, the paper is applied by a method of an autoregressive integrated moving average (ARIMA) model. This model is fitted to time series data both to better understand the data and to forecast future points in the series. Hereby, the methodology is selected by Vietnam's bestfit model ARIMA (2,3,1) and China's best-fit model ARIMA (2,3,5). Analytical data are collected from 1996 to 2017, the sample fitted the model and is statistically significant. The result show the forecast result for next years. The Vietnamese and Chinese economy are the open economies and have domestic credit greatly contributed to economic growth. These results are the basis for policymakers to have a general view and define the impact of domestic credit growth on GDP between the two countries.

A direct application of autoregressive (AR) models with independent and identically distributed (iid) errors is sometimes inadequate to fit the time series data well. A natural alternative is further to assume the model errors following an AR process, whose structure however has essential impacts on the statistical inferences related to the autoregressive models. In this paper, we construct a new unified test for checking the AR error structure based on the empirical likelihood method. The proposed test is desirable because its limit distribution is always chi-squared regardless of whether the autoregressive model is stationary or non-stationary, with or without an intercept term. Some simulations are also provided to illustrate the finite sample performance of this test. Finally, we apply the proposed test to a financial real data set.

Nonlinearity and spatial autocorrelation are common features observed in marine fish datasets but are often ignored or not considered simultaneously in modeling. Both features are often present within ecological data obtained across extensive spatial and temporal domains. A case study and a simulation were conducted to evaluate the necessity of considering both characteristics in marine species distribution modeling. We examined seven years of weakfish (Cynoscion regalis) survey catch rates along the Atlantic coast, and five types of statistical models were formulated using a delta model approach because of the high percentage of zero catches in the dataset. The delta spatial generalized additive model (GAM) confirmed the presence of nonlinear relationships with explanatory variables, and results from 3-fold cross-validation indicated that the delta spatial GAM yielded the smallest training and testing errors. Spatial maps of residuals also showed that the delta spatial GAM decreased the spatial autocorrelation in the data. The simulation study found that the spatial GAM over competes other models based on the mean squared error in all scenarios. That indicates that the recommended model not just works well for the NEAMAP survey but also for other cases as in the simulated scenarios.

Temperature is one of the most influential weather variables necessary for numerous studies, such as climate change, integrated water resources management, and water scarcity, among others. The temperature and precipitation are relevant in river basins because they may be particularly affected by modifications in the variability, for example, due to climate change. We developed a stochastic model for daily precipitation occurrences and their influence on maximum and minimum temperatures with a straightforward approach. The Markov model has been used to determine everyday occurrences of rainfall. Moreover, we developed a multisite multivariate autoregressive model to represent the short-term memory of daily temperature, called MASCV. The reduction of parameters is an essential factor addressed in this approach. For this reason, the normalization of the temperatures was performed through different nonparametric transformations. The case study is the Jucar River Basin in Spain. The multisite multivariate stochastic model of two states and a lag-one accurately represents both occurrences as well as maximum and minimum temperature. The simulation and generation of occurrences and temperature is considered a continuous multivariate stochastic process. Additionally, time series of multiple correlated climate variables are completed. Therefore, we simplify the complexity and reduce the computational time for the simulation.

DC arc faults are dangerous to photovoltaic (PV) systems and can cause serious electric fire hazards and property damage. Because the PV inverter works in a high−frequency pulse width modulation (PWM) control mode, the arc fault detection is prone to nuisance tripping due to PV inverter noises. An arc fault detection method based on the autoregressive (AR) model is proposed. A test platform collects the database of this research according to the UL1699B standard, in which three different types of PV inverters are taken into consideration to make it more generalized. The arc current can be considered a nonstationary random signal while the noise of the PV inverter is not. According to the difference in randomness features between an arc and the noise, a detection method based on the AR model is proposed. The Burg algorithm is used to determine model coefficients, while the Akaike Information Criterion (AIC) is applied to explore the best order of the proposed model. The correlation coefficient difference of the model coefficients plays a role as a criterion to identify if there is an arc fault. Moreover, a prototype circuit based on the TMS320F28033 MCU is built for algorithm verification. Test results show that the proposed algorithm can identify an arc fault without a false positive under different PV inverter conditions. The fault clearing time is between 60 ms to 80 ms, which can meet the requirement of 200 ms specified by the standard.

Mean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and disadvantages. Previous works studied how the implementation settings can influence the performance of these techniques; nevertheless, the estimation results have never been fully evaluated when the power density spectrum is in a low-frequency zone, as happens to the surface electromyography (sEMG) spectrum during muscle fatigue. The latter is therefore the objective of this study that has compared the Welch and the autoregressive parametric approaches on synthetic sEMG signals simulating severe muscle fatigue. Moreover, the sensitivity of both the approaches to the observation duration and to the level of noise has been analyzed. Results showed that the mean frequency greatly depends on the noise level, and that for Signal to Noise Ratio (SNR) less than 10dB the errors make the estimate unacceptable. On the other hand, the error in calculating the median frequency is always in the range 2–10 Hz, so this parameter should be preferred in the tracking of muscle fatigue. Results show that the autoregressive model always outperforms the Welch technique, and that the 3rd order continuously produced accurate and precise estimates; consequently, the latter should be used when analyzing severe fatiguing contraction.

Non-intrusive sleep monitoring is critical for certain populations such as severely disabled autistic children. Nocturnal disturbance analysis is an important diagnostic tool for assessing sleep issues. The objective of this paper is to detect and minimize the effects of motion artifacts in signals recorded via an unobtrusive electromechanical film-based ballistocardiogram (BCG) sensor integrated into a smart bed system. The goal is to have a reliable estimation of beat-to-beat (B-B) interval. The proposed algorithm includes two main stages: a motion detection algorithm followed by a motion artifact removal system. Motion detection involves a sequential detection algorithm in which successive data frames are compared to two thresholds: upper and lower thresholds. Each motion corrupted frame can then be reconstructed by an approach that relies on a parametric model of the BCG signal. Exploiting the fact that the underlying BCG parameters (J-peak-to-J-peak interval, J-peak-to-K-peak amplitude, and the most significant frequency component) change slowly and are correlated across time; an autoregressive model-based tracking and Wiener smoother based parameter estimation strategies are proposed. The experimental results are presented to demonstrate the effectiveness of the proposed motion artifact detection and removal algorithms. The probability of detection of 96% and high average coverage of 84% with over 90% precision for the estimated B-B intervals are achieved on recordings for 19 h of sleep from three subjects. Our novel motion detection and removal methods demonstrate the feasibility of using bed-based BCG signals for providing a reliable unobtrusive way to estimate the B-B interval in the presence of motion.

To detect median filtering forensics, a four-feature ensemble including the median filter residual autoregressive (MFR AR) model, statistical properties, gradient-edge line, and HU invariant moments of an image were used to propose an improved feature vector. The defined novel feature vector was trained on a support vector machine (SVM) classifier for median filtering detection (MFD) of forgery images. The performance of the proposed MFD scheme was measured with several types of images: median filtered (window size: {3 × 3, 5 × 5, composite (3 × 3, 5 × 5)}), JPEG compressed (quality factor: 90) after median filtered, rotated (counterclockwise: 5%), and noise added (salt-pepper: 5%) which has been re-altered in various ways. Experimental results show high efficiency and performance of the MFD techniques. The area under the curve (AUC) by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed MFD scheme are 0.9 upper with the trained SVM classifier. Thus, the grade evaluation of the proposed scheme is ``Excellent (A)”.