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In this paper, we are interested in the order estimation of an autoregressive model using the information criterion developed by El Matouat and Hallin (1996), which is based on stochastic complexity. This criterion is a generalization of the Hannan and Quinn criterion and provides a convergence of the model order estimator, but it depends on a parameter that is sensitive to the sample size. In order to select the exact order of the candidate model, we propose a method for identifying the values of this parameter from the sample using the information contained in sub-samples of increasing size. To study the performance of the proposed method in comparison with the usual criteria, we simulated samples from autoregressive models on which we applied our procedure. Simulation results support the relevance of our procedure when compared to the Akaike criterion, the Hannan and Quinn criterion, and the Schwarz criterion. [ABSTRACT FROM AUTHOR]

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.

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 % . [ABSTRACT FROM AUTHOR]

Introduction: Functional 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. Methods: In 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 Autoregressivemodel) 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. Results: Conventional whiteningmethods, 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. Discussion: This 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. [ABSTRACT FROM AUTHOR]

Power conversion systems for wireless power transfer (WPT) applications have demanding requirements for continuity of service, besides being operated with stressing environmental conditions. Diagnostic and prognostic programs are thus quite useful and this work shows a novel approach based on the analysis of spectra of an autoregressive (AR) model to recognize a wide range of faulty devices, including incipient faults, when deviations from nominal parameters begin to manifest. AR modeling provides cleaner and easier to interpret spectra, where only the salient features remain, and they are more sensitive to variations in the corresponding time domain waveforms. A log spectral distance is calculated that successfully separates healthy and faulty states of the feeding single-phase inverter, even in challenging scenarios of poor signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Diagnostic checking is an important predefined step before using autoregressive models. Although many portmanteau tests were proposed for diagnostic checking, they still struggle with the issue of significant size distortion. In this paper, we develop new diagnostic checking methods based on jackknife empirical likelihood. It is demonstrated that the suggested testing statistics asymptotically have a typical chi-squared distribution. To verify the performance of the finite sample, some simulations are constructed. Additionally, a real example of five agricultural futures is provided to illustrate the merits of our diagnostic checking procedure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Uncertain time series models constitute a pivotal tool for analyzing phenomena evolving over time. However, most of the prevailing research centers on parameter models, which exhibit limitations in addressing intricate temporal dynamics. To address this gap, this paper introduces a non-parametric uncertain time series model. Primarily, we propose the definition of the non-parametric uncertain time series models. Subsequently, tailored to the non-parametric uncertain additive autoregressive model, we propose a non-parametric estimation method based on linear polynomial splines. Following this, the effectiveness of these estimations is verified through residual analysis and uncertain hypothesis tests. Finally, we apply the introduced non-parametric estimation approach to the Brent crude oil spot price. [ABSTRACT FROM AUTHOR]

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

Nitrogen dioxide (NO2) is one of the pollutants that can cause potential damage to the ecosystem. NO2 emitted from vehicles forms the primary precursor for ground-level ozone. In this study, an analysis of the daily average of NO2 concentration with meteorology measured for two years 2021 and 2022 is being carried out. It is evident from the analysis that NO2 concentration followed an apparent diurnal pattern with a maximum value in the morning hours and a minimum during the afternoon hours. Summer months recorded the highest, and North East Monsoon (NEM) recorded the lowest values of NO2. A statistically significant positive correlation was found between NO2 and Temperature. An autoregressive model was formulated to forecast the daily average values of NO2 concentration. Unit root test was performed to check the stationarity of the data points, which is important in determining trends and seasonal changes. From the model procedure, the order that best fits the data was identified as AR (4), in which the process has the current value based on the previous three values. The Akaike Information Criterion (AIC) and Schwartz Criterion (SC), which are estimators of prediction error for AR (4), are low. The Jarque confirmed the normal distribution-Bera test, which again approves the satisfactoriness of the model.. [ABSTRACT FROM AUTHOR]

Speech involves the synchronization of the brain and the oral articulators. Inner speech, also known as imagined speech or covert speech, refers to thinking in the form of sound without intentional movement of the lips, tongue, or hands. Decoding human thoughts is a powerful technique that can help individuals who have lost the ability to speak. This paper introduces a high-performance brain wave decoder based on inner speech, using a novel feature extraction method. The approach combined Support Vector Machine (SVM) and multi-wavelet feature extraction techniques to decode two EEG-based inner speech datasets (Data 1 and Data 2) into internally spoken words. The proposed approach achieved an overall classification accuracy of 68.20%, precision of 68.22%, recall of 68.20%, and F1-score of 68.21% for Data 1, and accuracy of 97.5%, precision of 97.73%, recall of 97.50%, and F1-score of 97.61% for Data 2. Additionally, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) demonstrated the validity of the proposed approach for classifying inner speech commands by achieving a macro-average of 78.76% and 99.32% for Data 1 and Data 2, respectively. The EEG-based inner speech classification method proposed in this research has the potential to improve communication for patients with speech disorders, mutism, cognitive development issues, executive function problems, and mental disorder.

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.

Accurate prediction models of thermal errors are very useful for improving the machining accuracy of machine tools; it is also the core of thermal error compensation technology. Often, it is preferable to predict thermal deformation using a dynamic model, as opposed to computational inaccuracy and non-robustness existing in the static model. Autoregressive models are one of the most commonly used dynamic models. However, the autoregressive model needs to measure the thermal error online, which can be intrusive to the production process and reduce production efficiency. This paper presents a new exogenous input autoregressive modeling approach based on mixed variables (MV-ARX) in CNC Swiss lathes. In addition, offline prediction is achieved by replacing online measurements with estimates of thermal errors. The effects of factors on thermal error, such as ambient temperature and spindle speed, are analyzed through thermal characteristic experiments. The K-means clustering method was used to select the thermal critical point, and the exogenous input autoregressive prediction model was optimized by combining the selected temperature variables with the spindle speed to improve the accuracy and robustness of offline prediction. Compared with the model based on temperature-variable autoregression (TV-ARX) and multivariate linear regression (MLR), the proposed model shows better prediction performance. The offline prediction of thermal errors also showed good performance under non-training conditions, with an offline prediction accuracy of up to 83.52%. The modeling method proposed in this work may pave the way for improving the prediction of other errors with similar nonlinear hysteresis dynamical systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A generalized autoregressive (GNAR) model fusing both linearity and nonlinearity is proposed to solve the complex nonlinear time series modeling problem. First, the mathematical model of the GNAR model is established while the mathematical mechanism and physical meaning of the GNAR model are both expounded from the two aspects of Weierstrass theory and Volterra theory. Then, an improved least squares parameter estimation method, namely robust Residuals Adjusted Least Squares (RALS) method, is introduced and its process is successionally proposed to improve the anti-outlier performance of the GNAR model. Next, the mathematical model of the computational complexity for the GNAR model is established and the complexity model is introduced into the AIC to propose an improved AIC (iAIC). Finally, a dataset containing 23 records is established and experiments are carried out. The results show that the GNAR model with RALS estimator has high fitting accuracy. The Mean Square Error (MSE) of the series predicted by the GNAR model with Least Squares (LS) estimator is 325.3% higher than that of the series predicted by the GNAR model with RALS estimator at most. The effectiveness of order determination method reaches 82.61%. Therefore, the GNAR model together with its parameter estimation method and order determination method proposed in this paper are effective. [ABSTRACT FROM AUTHOR]

This research aims to derive the average run length (ARL) evaluation of the double exponentially weighted moving average (double EWMA) control chart for observation data that follows exponential white noise in a time series model with an autoregressive model. Since most real-world data is automatically correlated, autoregressive models are available. Comparisons were made between the ARLs obtained using the explicit formula and the numerical integral equation (NIE) approach. The results showed that the explicit formula's use of the ARL outperformed the NIE approach in terms of computation time. After that, the efficacy of the exponentially weighted moving average (EWMA) and double EWMA charts is then compared using the suggested explicit ARL formula. The ARL of the double EWMA chart was found to perform better than the ARL of the EWMA chart in all situations. It also uses natural gas and diesel prices on stock exchanges around the world as cases studies. The results show that the double EWMA chart has better detection sensitivity than the EWMA chart, and the results are consistent with the experimental results. As a result, the sensitivity of the double EWMA chart in detecting changes makes it a good alternative for monitoring processes with real-world data. [ABSTRACT FROM AUTHOR]

The study of multivariate time series forecasting (MTSF) problems has high significance in many areas, such as industrial forecasting and traffic flow forecasting. Traditional forecasting models pay more attention to the temporal features of variables and lack depth in extracting spatial and spatiotemporal features between variables. In this paper, a novel model based on the Transformer, convolutional neural network (CNN), and long short-term memory (LSTM) network is proposed to address the issues. The model first extracts the spatial feature vectors through the proposed Multi-kernel CNN. Then it fully extracts the temporal information by the Encoder layer that consists of the Transformer encoder layer and the LSTM network, which can also obtain the potential spatiotemporal correlation. To extract more feature information, we stack multiple Encoder layers. Finally, the output is decoded by the Decoder layer composed of the ReLU activation function and the Linear layer. To further improve the model's robustness, we also integrate an autoregressive model. In model evaluation, the proposed model achieves significant performance improvements over the current benchmark methods for MTSF tasks on four datasets. Further experiments demonstrate that the model can be used for long-horizon forecasting and achieve satisfactory results on the yield forecasting of test items (our private dataset, TIOB). [ABSTRACT FROM AUTHOR]

Energy costs represent a significant share of the total cost of ownership in high-performance computing systems. Using a unique data set collected by massive sensor networks in a petascale national supercomputing center, we first present an explanatory model to identify key factors affecting energy consumption in supercomputing. Our analytic results show that workload distribution among the nodes has significant effects and could effectively be leveraged to improve energy efficiency. We then establish the high model performance using in-sample and out-of-sample analyses and develop prescriptive models for energy-optimal runtime workload management. We present four dynamic resource management methodologies (packing, load balancing, threshold-based switching, and energy optimization), model their application at two levels (within-rack and cross-rack resource allocation), and explore runtime resource redistribution policies for jobs under the computational steering and comparatively evaluate strategies that use computational steering with those that do not. Our experimental results lead to a threshold strategy that yields near-optimal energy efficiency under all workload conditions. We further calibrate the energy-optimal resource allocations over the full range of workloads and present a bi-criteria evaluation to consider energy consumption and job performance tradeoffs. We conclude with implementation guidelines and policy insights into energy-efficient computing resource management in large supercomputing centers. Energy costs represent a significant share of the total cost of ownership in high-performance computing (HPC) systems. Using a unique data set collected by massive sensor networks in a petascale national supercomputing center, we first present an explanatory model to identify key factors that affect energy consumption in supercomputing. Our analytic results show that, not only does computing node utilization significantly affect energy consumption, workload distribution among the nodes also has significant effects and could effectively be leveraged to improve energy efficiency. Next, we establish the high model performance using in-sample and out-of-sample analyses. We then develop prescriptive models for energy-optimal runtime workload management and extend the models to consider energy consumption and job performance tradeoffs. Specifically, we present four dynamic resource management methodologies (packing, load balancing, threshold-based switching, and energy optimization), model their application at two levels (purely within-rack and jointly cross-rack resource allocation), and explore runtime resource redistribution policies for jobs under the emergent principle of computational steering and comparatively evaluate strategies that use computational steering with those that do not. Our experimental studies show that packing is preferred when the total workload of a rack is higher than a threshold and load balancing is preferred when it is lower. These results lead to a threshold strategy that yields near-optimal energy efficiency under all workload conditions. We further calibrate the energy-optimal resource allocations over the full range of workloads and present a bicriteria evaluation to consider energy consumption and job performance tradeoffs. We demonstrate significant energy savings of our proposed strategies under various workload conditions. We conclude with implementation guidelines and policy insights into energy-efficient computing resource management in large supercomputing data centers. History: Olivia Liu Sheng, Senior Editor; Xiaobai Li, Associate Editor. Funding: The authors thank the National Supercomputing Center of Singapore for enabling and supporting this research. J. Li received support from the National Natural Science Foundation of China [Grants 71901169, 72032006, and T2293774]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2023.1208. [ABSTRACT FROM AUTHOR]

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 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.

【Objective】 This study aims to analyze the impact of vegetation stability on water storage and meteorological factors in the Mongolian plateau. 【Method】 The analysis was based on data measured from 2002 to 2015. The GIMMS NDVI, monthly total water storage anomaly (TWSA), temperature, rainfall, and the autoregressive model was used to quantify the short-term impact of vegetation stability on TWSA and meteorological factors. 【Result】 The vegetation index NDVI in the plateau exhibited a strong correlation with temperature and precipitation. The areas that have strong correlation between NDVI and TWSA are primarily located in the central areas in the plateau. Most vegetation types show strong stability resilience, particularly in the Northeast, North, and center of the plateau. Woodland, meadow steppe, and typical grassland exhibited strong stability resilience. Most areas showed insignificant stability resistance to TWSA, and only 33.1% of vegetation exhibited strong stability resistance to TWSA. The areas with significant vegetation stability resistance were in the center, the East, and typical grassland and desert steppe areas in the Northwest. 【Conclusion】 The autoregressive model effectively represents the response of vegetation stability to water storage and meteorological factors in the plateau. The combined influence of precipitation and temperature plays a significant role in vegetation growth; areas with high water storage have a greater impact on vegetation in the plateau.

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.

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data-driven parametric reduced-order models (ROMs) in state-space form are valuable tools for rapid aeroelastic (AE) analysis and aerostructure control synthesis. However, the issue of state inconsistence (significant variations in model parameters over tradespaces) makes ROMs noninterpolatable, and therefore unable to accommodate use over broad flight parameter space. This paper presents a holistic framework that combines a system identification technique with state-consistence enforcement (SCE) and a genetic algorithm (GA) for the automated development of interpolatable AE ROMs across broad flight regimes. The SCE technique introduces a regularization term to the AutoRegressive model with eXogenous inputs (ARX) to specifically penalize model parameter variation between flight conditions. The GA autonomously guides the ROM development process toward optimal SCE-ARX hyperparameter selection that balances between model parameter variations and ROM accuracy. The GA-guided SCE-ARX approach is applied to build a parametric AE-ROM database at selected flight conditions, which, because of its state consistence, can be interpolated to create ROMs at any interstitial conditions, where training data or ROMs are not initially available, hence rapidly establishing the full coverage of the entire parameter space. The ROMs generated by the proposed method are compared with those by ARX, SCE, and GA-guided ARX in prediction accuracy. The individual and combined effects of SCE and GA on model parameter variation and ROM interpolatability are thoroughly investigated. The present method demonstrates the most accurate and robust performance for parametric ROM construction across the broad flight envelope. [ABSTRACT FROM AUTHOR]

For the accuracy of the autoregressive model, variable selection and model order are two main problems. We hope to select variables and determine the order at the same time. The improved adaptive LASSO method (IALASSO) not only notices the sensitivity of the time series, but pays attention to the influence of the lag order. We show that the IALASSO estimator enjoys asymptotic properties under certain conditions. Simulation studies demonstrate that the IALASSO estimator is superior than the other method. Finally, by applying IALASSO to actual time series data, the prediction accuracy is higher. [ABSTRACT FROM AUTHOR]

Abstract: Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.

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.

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.

Autoregressive (AR) models are useful in time series analysis. Inferences under such models are distorted in the presence of measurement error, a common feature in applications. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We consider two measurement error models to describe different data contamination scenarios. We propose an estimating equation approach to estimate the AR model parameters with measurement error effects accounted for. We further discuss forecasting using the proposed method. Our work is inspired by COVID-19 data, which are error-contaminated due to multiple reasons including those related to asymptomatic cases and varying incubation periods. We implement the proposed method by conducting sensitivity analyses and forecasting the fatality rate of COVID-19 over time for the four most populated provinces in Canada. The results suggest that incorporating or not incorporating measurement error effects may yield rather different results for parameter estimation and forecasting. [ABSTRACT FROM AUTHOR]

The time-inhomogeneous autoregressive model AR(1) is studied, which is the process of the form Xn+1 = αnXn + εn, where αn are constants, and εn are independent random variables. Conditions on αn and distributions of εn are established that guarantee the geometric recurrence of the process. This result is applied to estimate the stability of n-steps transition probabilities for two autoregressive processes X(1) and X(2) assuming that both αn(i), i ∈ {1, 2}, and distributions of εn(i), i ∈ {1, 2}, are close enough. [ABSTRACT FROM AUTHOR]

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.

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.

The prediction of high-speed railway bridge pier settlement is important for the safety of high-speed railway engineering. At present, a common method in settlement prediction is the curve fitting model in single prediction models. However, it may be difficult to describe the settlement rule of high-speed railway bridge piers using a curve fitting model with limited observation data during time-constrained construction periods. Moreover, relying on only a single prediction model usually does not allow for full exploration of the potential information in the data and poses the problem of poor stability and applicability. To solve this issue, a combined prediction model that uses the optimal nonnegative variable weight combination based on robust weighted total least-squares autoregression (RWTLS-AR) and adaptive dynamic cubic exponential smoothing (ADCES) is proposed to combine the advantages of two single prediction models. The RWTLS-AR model, using a robust weighted total least-squares method, has high prediction accuracy in the case of fewer observation data. At the same time, the adaptive dynamic judgment mechanism is established using the ADCES model to improve stability. The proposed model is applied to the settlement prediction of high-speed railway bridge pier, and three sets of observation data are used for evaluation. A comparison is made with two single prediction models and three other combined prediction models. The results show that the mean absolute error, root-mean-square error, and mean absolute percentage error of the proposed model are respectively 0.092 mm, 0.101, mm and 5.936% in the first set of observations, 0.099 mm, 0.118 mm, and 6.592% in the second set of observations, and 0.177 mm, 0.203 mm, and 15.914% in the third set of observations. This indicates that the proposed model is more accurate and stable than all the aforementioned prediction models. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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.

BackgroundZika virus (ZIKV) is an emerging mosquito-borne arbovirus that can produce serious public health consequences. In 2016, ZIKV caused an epidemic in many countries around the world, including the United States. ZIKV surveillance and vector control is essential to combating future epidemics. However, challenges relating to the timely publication of case reports significantly limit the effectiveness of current surveillance methods. In many countries with poor infrastructure, established systems for case reporting often do not exist. Previous studies investigating the H1N1 pandemic, general influenza and the recent Ebola outbreak have demonstrated that time- and geo-tagged Twitter data, which is immediately available, can be utilized to overcome these limitations.MethodsIn this study, we employed a recently developed system called Cloudberry to filter a random sample of Twitter data to investigate the feasibility of using such data for ZIKV epidemic tracking on a national and state (Florida) level. Two auto-regressive models were calibrated using weekly ZIKV case counts and zika tweets in order to estimate weekly ZIKV cases 1 week in advance.ResultsWhile models tended to over-predict at low case counts and under-predict at extreme high counts, a comparison of predicted versus observed weekly ZIKV case counts following model calibration demonstrated overall reasonable predictive accuracy, with an R2 of 0.74 for the Florida model and 0.70 for the U.S.ModelTime-series analysis of predicted and observed ZIKV cases following internal cross-validation exhibited very similar patterns, demonstrating reasonable model performance. Spatially, the distribution of cumulative ZIKV case counts (local- & travel-related) and zika tweets across all 50 U.S. states showed a high correlation (r = 0.73) after adjusting for population.ConclusionsThis study demonstrates the value of utilizing Twitter data for the purposes of disease surveillance. This is of high value to epidemiologist and public health officials charged with protecting the public during future outbreaks.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]