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With the capability for single-receiver AR (Ambiguity Resolution), real-time GNSS (Global Navigation Satellite System) satellite integer clock has been the research hotspot of GNSS community in the past decade. Robust ambiguity datum is one prerequisite for high-quality integer clock. In this paper, with undifferenced AR model derived, a robust ambiguity datum definition method is proposed to isolate the signal biases from the integer ambiguity. Firstly, based on the ambiguity graph, the ambiguity datum is selected with the minimum spanning tree algorithm. Secondly, the ambiguity datum is transferred to the subsequent epochs through signal bias connection. Moreover, additional datum ambiguities are introduced to accommodate the arising of receiver and satellite in real-time circumstance. With the combination of ambiguity graph and signal bias, the efficient definition and smooth transition of ambiguity datum are assured for the real-time processing. To validate the approach, simulated real-time integer clock estimation for GPS/Galileo/BDS-3 satellites are conducted with one-month (January 2022) data from about 100 global IGS (International GNSS Service) network stations. Experiments show that the proposed robust ambiguity datum can produce accurate real-time integer clock. The average WL (Wide-Lane) and NL (Narrow-Lane) fixed rates exceed 99 % and 95 %, respectively, for most of the GPS/Galileo satellites. While comparable WL fixed rate is achieved for the BDS-3 satellites, the NL fixed rate is about 5 % lower as a result of poor orbit quality and imperfect measurement model. With eclipsing satellites excluded, the standard deviations between real-time integer clock and post-processed reference clock reach 0.018 ns and 0.016 ns for the GPS and Galileo constellations, respectively, which are 28 % and 24 % smaller than those of corresponding float clocks. In contrast, the standard deviations of float and integer clocks are similar for the BDS-3 constellation. Further improvements on the background models, including unconservative force modeling, satellite attitude modeling and antenna calibration are required to achieve ultimate accuracy, especially for the BDS-3 satellites. [ABSTRACT FROM AUTHOR]

This paper studies the behaviour of the local linear (LL) estimator for regression with nonparametrically generated regressors under weak dependence conditions, effectively extending the main result of Mammen et al. (2012) to the case with strongly mixing (or α -mixing) data. Specifically, we establish that the convergence rate in their Theorem 1 carries over to the case with geometrically α -mixing data. In contrast, this convergence rate does not necessarily remain the same for polynomially α -mixing data. We then apply the obtained uniform stochastic expansion of the second-step LL estimator to derive normal approximations for the LL estimator of a nonparametric censored autoregressive model and the three-step nonparametric estimator of the risk–return regression in finance. A rule for bandwidth selection in nonparametric regressions with estimated covariates to obtain valid inference is also suggested. We then provide a simulation study and a real data application to illustrate the practical relevance of the proposed approach. Lastly, technically speaking, to establish a uniform bound for the upper tail probability of a summation involving polynomially strong-mixing empirical processes, we propose a new exponential inequality, which plays a pivotal role in the proof of the main theorem and is of independent interest. • This paper studies the behaviour of the local linear (LL) estimator for regression with nonparametrically generated regressors under weak dependence conditions. • Derivation of normal approximations for the LL estimator of a nonparametric censored autoregressive model and the three-step nonparametric estimator of the risk–return regression in finance. • A rule for bandwidth selection in nonparametric regressions with estimated covariates to obtain valid inference is also suggested. • A simulation study and a real data application are provided to illustrate the practical relevance of the proposed approach. • A new exponential inequality is also proposed to establish a uniform bound for the upper tail probability of a summation involving polynomially strong-mixing empirical processes. [ABSTRACT FROM AUTHOR]

This paper proposes a P ositional- E ncoded A synchronous Auto R egression (PEAR) method for satellite anomaly detection. We empirically observe that a single classification model can hardly detect unknown anomalous situations and neglect the Markov nature of temporal satellite data. To address this, we adopt an autoregressive model to deal with the prediction of unknown anomaly for satellite data. We further propose a non-uniform temporal encoding method for asynchronous data and a median filtering method for more accurate detection. To reduce the effect of outliers, we employ an adaptive threshold selection method to achieve a more robust classification boundary. We test the proposed method on the time series prediction models including LSTM and transformers and we further employ the positional encoding strategy to improve the modeling capabilities of the Transformer model on high-frequency information. Experiments on real satellite data demonstrate that the proposed PEAR method outperforms the baseline method by 55.79%. • We propose an autoregressive model to predict the unknown satellite anomaly. • We employ the positional encoding strategy to fit high-frequency information. • Extensive experiments demonstrate the superior performances of PEAR. [ABSTRACT FROM AUTHOR]

This paper collects monthly data for the United States from January 2005 to April 2021 and develops a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model to investigate the time-varying relationship between climate policy uncertainty, oil prices, and renewable energy consumption. In addition, this paper discusses the dynamic impact of climate policy uncertainty on five types of renewable energy consumption. Our results show that the relationship between them is time-varying. In most periods, climate policy uncertainty positively affects oil prices in the short and medium term, and positively affects total renewable energy consumption in the short and long term. In addition, the impact of climate policy uncertainty on the consumption of the five types of renewable energy is heterogeneous. The results of the robustness tests indicate that our results are robust and our study has important implications for the development of relevant policies. [ABSTRACT FROM AUTHOR]

A portmanteau-type statistic based on sample covariance is proposed to test for the existence of the unit-root of an autoregressive model under a heavy-tailed linear noise, where the noise ɛ t = ∑ j = 0 ∞ d j η t − j and { η t } is a sequence of i.i.d. innovations belonging to the domain of attraction of an α -stable distribution for some α ∈ (0 , 2). The proposed procedure is nonparametric and easy to implement. Under certain regular assumptions, the limit distribution of the statistics is shown to be a functional of a standard stable distribution. Further, the good finite sample studies show that the new test outperforms PP, DF and ADF tests in size and power. Applications to the daily world crude oil price and 3-month AA financial commercial paper rate are also given to illustrate the performance of the new test. • The suggested references have been cited in the paper. • Details about the simulations are provided in the revision. • We have deleted "ARIMA" on page 4 to avoid the confusion. • Two new data sets are presented to illustrate the Hill estimator. [ABSTRACT FROM AUTHOR]

In this paper, we consider Poisson thinning Integer-valued time series models, namely integer-valued moving average model (INMA) and Integer-valued Autoregressive Moving Average model (INARMA), and their relationship with cluster point processes, the Cox point process and the dynamic contagion process. We derive the probability generating functionals of INARMA models and compare to that of cluster point processes. The main aim of this paper is to prove that, under a specific parametric setting, INMA and INARMA models are just discrete versions of continuous cluster point processes and hence converge weakly when the length of subintervals goes to zero. [ABSTRACT FROM AUTHOR]

We derive strong mixing conditions for many existing discrete-valued time series models that include exogenous covariates in the dynamic. Our main contribution is to study how a mixing condition on the covariate process transfers to a mixing condition for the response. Using a coupling method, we first derive mixing conditions for some Markov chains in random environments, which gives a first result for some autoregressive categorical processes with strictly exogenous regressors. Our result is then extended to some infinite memory categorical processes. In the second part of the paper, we study autoregressive models for which the covariates are sequentially exogenous. Using a general random mapping approach on finite sets, we get explicit mixing conditions that can be checked for many categorical time series found in the literature, including multinomial autoregressive processes, ordinal time series and dynamic multiple choice models. We also study some autoregressive count time series using a somewhat different contraction argument. Our contribution fills an important gap for such models, presented here under a more general form, since such a strong mixing condition is often assumed in some recent works but no general approach is available to check it. [ABSTRACT FROM AUTHOR]

In this paper, we consider an inference problem for the first order autoregressive process driven by a given long memory stationary Gaussian noise. Suppose that the covariance function of the noise is positive and can be expressed as | k | 2 H − 2 times a positive function slowly varying at infinity. The fractional Gaussian noise and the fractional ARIMA(0, d , 0) model with d ∈ (0 , 1 2) and some others Gaussian noise are special examples that satisfy this assumption. We propose a moment estimator and prove the strong consistency, the asymptotic normality and the almost sure central limit theorem. Moreover, we give the upper Berry–Esséen bound by means of Fourth moment theorem. • Moment estimator better than LSE since LSE not consistent. • Moment estimator more simple than MLE, but rate of convergence less than MLE. • Berry–Esséen bound of moment estimator is shown by means of Fourth moment theorem. [ABSTRACT FROM AUTHOR]

• Soil respiration data are non-stationary and influenced by environmental factors. • Clustering operations can reduce non-stationarity in data. • Number of environmental factors has influence on the prediction of soil respiration. • Number of clustering has influence on the prediction of soil respiration. • Different clustering methods has influence on the prediction of soil respiration. • Soil respiration at different time periods can be well predicted. Soil respiration time series data exhibit obvious non-stationarity, with diurnal fluctuations and susceptibility to various environmental factors. While traditional autoregressive models like ARIMA boast high predictive accuracy, they are limited to stationary time series, and result in subpar performance when applied to non-stationary data. Besides, they are restricted to single-column time series. To address these limitations, a new model, Cluster-ARIMA, for soil respiration prediction is proposed in this paper. Initially, the soil respiration time series data is partitioned into distinct clusters using clustering techniques. Subsequently, SARIMA is employed to determine the cluster to which the data to be forecasted belongs. The multi-column input data are then transformed into a compressed core tensor using block Hankel tensors and Tucker decomposition within the corresponding clusters. Finally, ARIMA is utilized for prediction, yielding the ultimate forecast outcomes. The clustering procedure enhances inter-data correlation, improving prediction performance while reducing the data scope involved in forecasting, thereby enhancing the efficiency. Experimental results showed that the proposed model Cluster-ARIMA achieved a prediction accuracy of 98.3%. Moreover, compared with other prediction models such as DeepAR, WaveNet, DeepState, XGBoost and BHT, Cluster-ARIMA exhibited superior predictive accuracy across different environmental factors, clustering methodologies, and counts of clusters. [ABSTRACT FROM AUTHOR]

The spectra from singly ionized argon Ar II has significant diagnostic capability in the characterization and modelling of both magnetically-confined fusion and astrophysical plasmas. The literature has several pre-existing data sets for Ar + but this paper presents the results from 3 new atomic structure and electron-impact scattering models in order to better constrain the differences in atomic data and how they impact well-known plasma diagnostics. Several independent atomic structure methodologies are employed to calculate the energy levels and transition probabilities for each model. The first approach employs a relativistic Dirac–Coulomb Hamiltonian model, the second approach uses a semi-relativistic Breit–Pauli Hamiltonian with the mass-velocity, Darwin and spin–orbit corrections, and in a third case an ICFT approach. Three atomic structure models provide a foundation for Dirac R-matrix, a semi-relativistic ICFT (Intermediate Coupling Frame Transformation) and a Breit–Pauli R-Matrix with Pseudostates (BPRMPS) calculation. Synthetic spectra utilizing these three data sets are compared against measurements taken at the Compact Toroidal Hybrid (CTH) stellarator, and the total radiative power loss is also benchmarked against previous calculations. • Present Atomic Structure and Transition probabilities for 3 independent Ar II structure calculations. • Demonstrated convergence between 3 Separate R-Matrix methods (PDARC. BPRMPS and ICFT) for electron-impact excitation. • Resolved deviations in total power loss calculations of CR-Modelling of different collisional calculations. [ABSTRACT FROM AUTHOR]

Mesoscale Eddy (ME) is a widely recognized and significant oceanic phenomenon characterized by extensive energy exchange. Accurate predictions of the future geospatial distribution of ME are crucial for maritime activities. Deep learning (DL) methods for ME prediction have consistently outperformed classical mathematical models and numerical modeling approaches. However, current DL methods based on two-dimensional gridded data suffer from error accumulation issues due to their indirect prediction approach, which involves first predicting the future environmental field and then identifying the ME patterns within it. Additionally, these models ignore the potential influence of prior knowledge related to ME on prediction outcomes. To address these issues, this paper proposes a direct prediction DL network called ES-ConvGRU, which incorporates historical prior statistics. First, we conduct an analysis of ME data from the Northwest Pacific region, extracting prior statistical knowledge such as interannual variations and seasonal characteristics. Second, mining patterns for the spatiotemporal features of both ME sequence and sea surface environmental field sequence are established. Furthermore, a nonlinear approach for mapping prior statistical knowledge and a multi-step autoregressive prediction model are designed, facilitating the integration of prior knowledge with multidimensional spatiotemporal features for ME prediction. Finally, the performance and effectiveness of ES-ConvGRU are evaluated. It achieves an average pixel accuracy of 93.21%, a mean intersection over union of 81.48%, and a frequency-weighted intersection over union of 87.55% for the 7-day distribution prediction. The results show that ES-ConvGRU exhibits notable advantages compared to the existing indirect and direct prediction methods. • A direct predictive approach for mesoscale eddy distribution prediction is developed. • Prior seasonal statistics is considered for prediction. • An end-to-end deep prediction network architecture, ES-ConvGRU, is proposed. • The results show that ES-ConvGRU performs better than other predictive methods. [ABSTRACT FROM AUTHOR]

In this paper, we suggest an empirical likelihood-based test for the autoregressive coefficient of an integer-valued AR(1) model, i.e., INAR(1). We derive the limit distributions of the resulting test statistic under both null and alternative hypotheses. It turns out that regardless of whether the INAR process is stable or unstable, the statistic is always chi-squared distributed asymptotically under the null hypothesis, and as a result, it can offer unified inferences for the autoregressive coefficient. The performance of its finite sample is also demonstrated using simulations and an empirical example. • An empirical likelihood-based unified test for the coefficient of INAR(1) model. • The test statistic is always chi-squared distributed asymptotically. • The unified test is robust to the distribution of ϵ t. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) and Artificial Intelligence (AI) are technologies pioneers in innovation and alteration in several domains. AR allows the creation of an entirely new and interactive experience for users. However, there are several drawbacks in developing AR applications, such as the marker identification process and the creation of content itself. These are very time-consuming procedures and require ad-hoc development. The advantages of using AI to solve AR limitations have recently been explored in literature. Motivated by these findings, in this paper it is proposed DeepReality, a software toolkit plug-in for Unity 3D. It is conceived for allowing developers to integrate any Deep Learning (DL) models into Unity, through AR Foundation and Barracuda inference engine. DeepReality is aimed at simplifying and streamlining the usage of DL models in conjunction with AR. As such, users skilled in Unity and DL can easily create mobile applications (iOS and Android) to: extract visual features of real-world objects (framed with the device camera) via DL; Show on-screen content on top of those real-world objects, via AR. DeepReality performs object semantic processing within the scene, and extended semantic effects for incongruent objects, overcoming the environmental tracking, which is feature-based. In order to test DeepReality usability, experiments have been performed on the execution time and memory usage data, demonstrating the feasibility and possibility of integrating and using DNNs models in mobile applications for AR. The complexity analysis confirms that DeepReality can be completely executed on mobile devices. DeepReality is also open-source and it is freely available in the Unity asset store. By fostering accessible AI-AR integration, DeepReality addresses key shortcomings in existing approaches, encapsulating contributions such as versatile DL integration, open-source accessibility, operational validation, and comprehensive metrics analysis. DeepReality empowers developers to transcend boundaries, enriching AR applications with AI's transformative potential. Our proposed framework fosters benchmarking, comparison, and a future harmonised by AR-AI synergy. [Display omitted] • DeepReality is an open-source toolkit, freely available in the Unity asset store. • DeepReality integrates Deep Learning models for streamlined mobile apps. • DeepReality leverages AR Foundation and Barracuda inference engine. • It demonstrates efficient execution and memory usage in mobile AR. • Complexity analysis confirms that it can be fully executed on mobile devices. [ABSTRACT FROM AUTHOR]

• A comprehensive set of speckle noise suppression and vibration demodulation algorithms tailored for dynamic non-cooperative target measurement in frequency-scanning interferometry. • Analyzed the characteristics of speckle noise at instantaneous frequency, and provided the numerical solution for instantaneous frequency based on complex shifted Morlet wavelets. • Proposed a parameter-adjustable smoothing weight function for bidirectional prediction and correction base on the ARI model, coupled with smoothing through bidirectional filtering. • The surface vibration of black alumina was extracted through Kalman filtering, with an amplitude error of ± 17 nm and a frequency error of ± 0.021 Hz. • Restore the axial end face runout of the DC motor. Speckle noise significantly impairs the accuracy of vibration measurements for non-cooperative targets in frequency-scanning interferometry (FSI). To restore the real vibration, this paper proposes a comprehensive vibration demodulation algorithm based on the FSI. We established an FSI vibration model accounting for speckle noise. The extraction of instantaneous frequency from the interference signal is performed using a complex-shifted Morlet wavelet. An autoregressive differential model, incorporating a smooth weight function, is applied for bidirectional prediction to correct speckle noise. Finally, the true vibration of the target is restored through Kalman filtering. We validated the algorithm's accuracy using a Monte Carlo strategy. The experimental results show that the surface vibration of black aluminum oxide alloy and the axial runout of the DC motor during operation was successfully captured, and the time–frequency analysis results showed that this method has strong robustness against dense speckle noise. [ABSTRACT FROM AUTHOR]

Dynamic modeling and state of charge (SOC) estimation of lithium-ion batteries (LIBs) is a critical technology in the battery management system. Aiming at the essential nonlinear characteristics of LIBs and the time-varying operating state caused by the influence of the external environment including load changes and other factors, this paper proposes to adopt the state-dependent autoregressive model with exogenous inputs (SD-ARX) to describe its nonlinear dynamic characteristics. This type of model first constructs state signal quantities that can characterize the dynamic properties of LIBs and uses them as inputs to the radial basis function neural network to approximate the functional-type coefficients of the SD-ARX model, which is used to guide the model to represent the nonlinear dynamic properties of LIBs under different operating states. Benefiting from the model can be represented as a linear combination of nonlinear functions, the model parameters identified offline by a structured nonlinear parameter optimization method. Finally, the SOC is estimated online using an adaptive extended Kalman filter based on the established model. The results show that the method can reliably estimate the SOC of a battery under different working conditions and a relatively wide temperature range and achieves a balance between accuracy and real-time performance. • A state-dependent autoregressive model with exogenous inputs for battery modeling. • Optimization of parameters by structured nonlinear parameter optimization method. • Comparison with results from existing literature. • The model has good modeling accuracy and SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

Wind prediction technology has been the focus on the national research with the basis of the power system planning, a reference for power dispatch, and the optimal power flow distribution. Prediction technology is moving into the direction of controlling refinement with the development of the information technology, the artificial intelligence technology, and the improvement of edge computing devices. The wind farms can use real-time wind power prediction to improve their overall efficiency through advanced planning of the wind turbine adjustment and the pre-setting of the yaw, pitch, and generation excitation control systems. This paper proposes an innovative autoregressive dynamic adaptive (ARDA) model based on the improvement of the autoregressive (AR) model. The fixed parameter estimation method of the AR model is improved in the proposed model to a dynamically adaptive stepwise parameter estimation method. Meanwhile, the coefficients of the model are updated adaptively based on the characteristics of wind power data, which improves the accuracy of the proposed model. The prediction accuracy of the proposed model is further improved by the residual function. It was observed that the model adapts well to wind power data with different degrees of volatility. The ARDA model and two other models were tested by using stationary and fluctuating wind power data (unit: seconds), and the wind power prediction results at different forecasting step lengths were compared. It was observed that the ARDA model is more accurate, with faster calculation rate, and better dynamic adaptability to data fluctuations than the ARIMA and LSTM models. This paper proposes an important method for real-time power prediction that can be employed for the advanced control and improved the power generation of wind farms. • We develop a novel model called the ARDA model based on AR model. • The proposed model is self-learning and dynamically adaptive to data fluctuations. • Parameter estimation highlights neighbouring data and forgets distant data. • The residual function corrects the prediction result based on data fluctuations. [ABSTRACT FROM AUTHOR]

• Socio-economic factors are more important than risks in the allocation of RMT budget. • More affluent regions and areas of relatively high welfare spend more for the RMT. • Regions with high agricultural VA and more permanent crops land spend less on the RMT. • The income stabilization capacity of direct payments is an alternative to risk management. • The institutional environment and political economy processes have a key role in the allocation of budget. This paper analyses the factors influencing public expenditure on the EU's Risk Management Toolkit comparing six types of regional-level spatial autoregressive models, and finding that there is a predominance of socio-economic factors over risk and agro-ecological factors in the allocation of budget. Higher expenditure occurs in more affluent regions and in areas with clusters of relatively high welfare. Conversely, regions with high agricultural value added and a greater presence of permanent crops spend less on the Risk Management Toolkit, indicating a trade-off with private risk management products (e.g. insurance) and on-farm practices. A higher intensity of CAP subsidies tends to reduce expenditure on the Risk Management Toolkit, indicating that the income stabilization capacity of direct payments might be an alternative to risk management and indicating a substitution between policies. Moreover, a strong spatial dependence of the regional expenditures indicates a key role of the higher-level institutional environment and political economy processes in the allocation of budget. The paper concludes deriving policy implications for the new CAP reform which is characterized by higher flexibility in deciding what policy instruments to implement at the territorial level. [ABSTRACT FROM AUTHOR]

• A novel model-based clustering approach for sequential data, nTreeClus, is proposed. • nTreeClus introduces a Decision Tree Path encoder in an autoregressive manner. • The method's robustness to its only parameter (window size) has been examined. • nTreeClus shows competitive results compared to existing methods in sequence mining. The overwhelming presence of categorical/sequential data in diverse domains emphasizes the importance of sequence mining. The challenging nature of sequences proves the need for continuing research to find a more accurate and faster approach providing a better understanding of their (dis) similarities. This paper proposes a new Model-based approach for clustering sequence data, namely nTreeClus. The proposed method deploys Tree-based Learners, k -mers, and autoregressive models for categorical time series, culminating with a novel numerical representation of the categorical sequences. Adopting this new representation, we cluster sequences, considering the inherent patterns in categorical time series. Accordingly, the model showed robustness to its parameter. Under different simulated scenarios, nTreeClus improved the baseline methods for various internal and external cluster validation metrics for up to 10.7% and 2.7%, respectively. The empirical evaluation using synthetic and real datasets, protein sequences, and categorical time series showed that nTreeClus is competitive or superior to most state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The temporal aggregation (TA) and systematic sampling (SS) in continuous-valued time series were widely studied, while the TA and SS in integer-valued time series obtain very little attention. In this paper, we thoroughly discuss them based on the integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) process. On one hand, the TA and SS for INGARCH processes are studied, and related predictors are discussed. We also illustrate that the TA or SS provides approaches to estimate INGARCH processes with different frequencies. On the other hand, definitions of the strong and weak INGARCH processes are given. The aggregated INGARCH and sampled INGARCH processes are shown to be weak, that is, they satisfy a weak characterization of INGARCH processes in terms of linear projection. Quasi maximum likelihood and nonlinear least squares estimation methods are considered for the weak INGARCH processes. Related asymptotic properties and simulation results are discussed. Finally, an empirical example illustrates the applicability of our results. • Define strong and weak INGARCH processes for temporal aggregation for the first time. • Provide new methods for estimating and predicting the aggregated count time series. • Analyze an example to illustrate the applicability of our results. [ABSTRACT FROM AUTHOR]

With an ever-increasing share of intermittent renewable energy in the world's energy mix, there is an increasing need for advanced solar power forecasting models to optimize the operation and control of solar power plants. In order to justify the need for more elaborate forecast modeling, one must compare the performance of advanced models with naïve reference methods. On this point, a rigorous formalism using statistical tools, variational calculation and quantification of noise in the measurement is studied and five naïve reference forecasting methods are considered, among which there is a newly proposed approach called ARTU (a particular autoregressive model of order two). These methods do not require any training phase nor demand any (or almost no) historical data. Additionally, motivated by the well-known benefits of ensemble forecasting, a combination of these models is considered, and then validated using data from multiple sites with diverse climatological characteristics, based on various error metrics, among which some are rarely used in the field of solar energy. The most appropriate benchmarking method depends on the salient features of the variable being forecast (e.g., seasonality, cyclicity, or conditional heteoroscedasity) as well as the forecast horizon. Hence, to ensure a fair benchmarking, forecasters should endeavor to discover the most appropriate naïve reference method for their setup by testing all available options. Among the methods proposed in this paper, the combination and ARTU statistically offer the best results for the proposed study conditions. • Benchmark of six Statistical Reference Methods (SRM). • Direct multi-step forecast strategy without training phase. • Validation of results using data from multiple climates. • Theory mixing statistical tools, variational calculation and measurement error. • Combination of models and ARTU are the best performing models. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new first-order mixture integer-valued threshold autoregressive process, based on the binomial and negative binomial thinning operators. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares (CLS) and conditional maximum likelihood (CML) estimators are derived and the asymptotic properties of the estimators are established. The inference for the threshold parameter is obtained based on the CLS and CML score functions. Moreover, the Wald test is applied to detect the existence of the piecewise structure. Simulation studies are considered, along with an application: the number of criminal mischief incidents in the Pittsburgh dataset • First-Order mixture integer-valued threshold autoregressive process based on binomial thinning and negative binomial thinning. • Parameter estimation for BiNB-MTINAR(1) process. • Detect the existence of the piecewise structure. [ABSTRACT FROM AUTHOR]

Electro-optical tracking system which is used to track and aim at targets, is a key equipment in long-distance laser communication, astronomical observation, and so on. And the motion state of the target and the complicated external disturbance often limit the tracking accuracy of the system. In previous studies, target tracking and disturbance suppression are usually discussed separately. In this paper, in order to solve the tracking and disturbance problems together, the error-based observer (EOB) structure was proposed. It is a feedforward control method that only needs an error signal to observe the target trajectory and external disturbances. And it can be used to improve the tracking and anti-disturbance performance of the system without additional sensors. However, in the actual scene, the target motion and external disturbance faced by the electro-optical tracking system are often variable. So an adaptive error-based observer (AEOB) structure based on signal identification and adaptive compensation was proposed, which can reduce the effect of time-varying target motion and narrowband disturbance on tracking accuracy. And the adaptive process of AEOB contains AR parameter model identification with high precision and adaptive feedforward of variable parameter Q filter. In addition, an improved notch Q filter is proposed, which had stronger feedforward capabilities for narrowband signals. Finally, the effectiveness is verified by simulations and experiments of the electro-optical tracking system. • An AEOB control method for varying tracking and disturbance problems is proposed. • An improved Q filter design method enabling for easy parameter change is proposed. • The high-precision AR method is applied for identifying the observed signal. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most threatening risk factors for human health. The epidemiological literature has widely proved that exposure to high levels of PM 10 is associated with an increase in cardiovascular and respiratory events. In this paper, we investigate the relationship between air pollution and emergency department visits for cardiovascular and respiratory diseases. Since the relationship between air pollution and emergency care access is complex and may be subject to regime switches, especially due to the COVID-19 pandemic, we propose to use a threshold autoregressive model where the regime-switching mechanism is driven by temporal evolution. This allows us to identify one or more structural breaks and estimate the regime-changing relationship between emergency care accesses and PM 10. Moreover, we propose a novel way to account for long-term dependence in the dependent variable. Using daily data from the metropolitan area of Bologna (Italy), we show that the effect of the air pollutant changes over time, mostly due to the outbreak of the COVID-19 pandemic. • The relationship between air pollution and health is nonlinear. • PM 10 increases emergency visits for respiratory diseases. • Post-lockdown, the association between PM 10 and emergency visits worsens. [ABSTRACT FROM AUTHOR]

This paper estimates a 3-country DSGE model to identify the drivers of exchange rate volatility in small open economies (SOE). In addition to the usual cross-country linkages through trade and asset holdings, the model features common shocks that affect economies symmetrically. Using data from Jamaica, the US and the G-7 region (excluding the US), the paper finds that external financial shocks are the primary drivers of exchange rate fluctuations in the SOE. While domestic financial shocks are bigger contributors than US and G-7 specific shocks, shocks that are common across the US and the G-7 generally play the main role. Nonfinancial shocks, domestic and external, are inconsequential for exchange rate volatility. Inferences from a vector autoregressive model with exogenous variables are consistent with these results. • I identify the drivers of exchange rate volatility in an emerging market economy. • I estimates a DSGE model by using data from Jamaica, the US and the G-7 region. • External financial shocks are the primary drivers of exchange rate fluctuations. • Domestic financial shocks are bigger contributors than US and G-7 specific shocks. • Shocks that are common across the US and the G-7 generally play the main role. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has increased the unemployment issue and accelerated the digital transformation. Real-time data specific to ongoing revolution in applied economic analysis are increasingly demanded to anticipate changes in unemployment to improve decision-making. The aim of this paper is to test whether unemployment rate forecasts based on Google Trends data improve the predictions based only on macroeconomic indicators published with a longer time lag. The research has been carried out at the national level for Spain and Portugal, and the main novelty is the analysis of unemployment rate forecasts at the regional level for Spain using dynamic panel data models to implement the best policies to reduce unemployment. The keywords unemployment and job offers have been used in each language. The results obtained demonstrate the capacity of Google Trends data associated with unemployment to improve the predictions of unemployment rates in Spanish regions. Moreover, predictions based on Google Trends data at national level in Spain and Portugal are significantly more accurate than those based on autoregressive models for both countries. [ABSTRACT FROM AUTHOR]

The paper examines the integration between electricity and renewable energy certificate (REC) markets for India. The empirical investigation covers the factors influencing solar and non-solar REC markets, using an autoregressive distributed lag (ARDL) modeling approach. The variables used in this study are the traded volume of solar and non-solar RECs, the traded volume of electricity, wholesale electricity price, and prices of solar and non-solar RECs. The dataset contains monthly time series for the period January 2013–July 2020. The ARDL bounds test results show that the variables are cointegrated. The long-run ARDL estimates indicate that the traded volume of solar REC is influenced by the traded volume of electricity (positive effect), wholesale electricity price (negative effect), the traded volume of non-solar REC (positive effect), and price of solar REC (negative effect). However, the traded volume of non-solar REC is influenced by only traded volume (positive effect) and price (positive effect) of solar REC. Based on the findings of this study, few suggestions are discussed to strengthen the REC market in India. [Display omitted] • Impact of electricity market on renewable energy certificate (REC) market is examined for India. • Autoregressive distributed lag model is employed on a monthly time series data over January 2013–July 2020. • Only the traded volume of solar REC is influenced by traded volume and price of electricity. • Traded volume of solar and non-solar RECs is positively related to each other. • An increase in the price of solar REC raises traded volume of non-solar REC. [ABSTRACT FROM AUTHOR]

In this paper, we develop a method for handling nonignorable missing data in fitting time series models for longitudinal surveys. We assume that the response probability not only depends on auxiliary variables but also the current and past outcomes which are subject to missingness. Under a nonignorable missing mechanism, an observed likelihood estimation approach is proposed based on the distribution of the observed sample and the response probability. Also, we derive a series expansion approximation for an integral in the observed likelihood function. Results from simulation studies are presented to show the usefulness of the proposed methodology. An empirical example based on data from the AIDS Clinical Trial Group 193A Study is provided to illustrate the proposed method. • An observed likelihood approach is proposed for fitting time series models. • An accurate series expansion approximation of an integral is derived. • An application of the proposed method for the AIDS data is presented. [ABSTRACT FROM AUTHOR]

This paper examines the dynamical impact of market wide liquidity on the volatility by using the Markov switching-regime approach on the Heterogeneous Autoregressive model of the realized range-based volatility (MS-HAR-RRV-LIQ). First, based on the simple partition of the liquidity measures by their historical means, we find the impact of the market liquidity on the realized range-based volatility (RRV) is non-linear with significantly stronger when the market is fluctuating. Second, by utilizing the method of Markov regimes switching, we find that in sample results, the impact of market liquidity on RRV is dynamics with much stronger when the market is fluctuating. In addition, the impact of liquidity on RRV are persistent for both the fluctuating state and the stable state, which indicate that the impact of liquidity on volatility would remain in its original state unless there are major economic events. Third, in order to eliminate the hidden information of volatility in liquidity measures, we further decompose the liquidity measures to obtain the volatility-adjusted liquidity, and find that the impact of liquidity driven by the component of liquidity uncorrelated with volatility on market volatility is also dynamics with much stronger when the market is fluctuating. Finally, out-of-sample results suggest that the models of HAR-RRV-LIQ and their corresponding volatility-adjusted liquidity models with regimes switching can increase the forecasting ability significantly than those without regime switching. • This paper examines the dynamical impact of market wide liquidity on volatility. • The impact of the market wide liquidity on RRV is non-linear. • The impact of market wide liquidity on RRV is dynamics. • Our proposed models can increase the forecasting ability significantly. [ABSTRACT FROM AUTHOR]

Abstract This paper is devoted to a method for the forecasting of seasonal time series. The core of our approach is based on the fuzzy transform and fuzzy natural logic (FNL) techniques. Under the assumption that a time series can be additively decomposed into a trend-cycle, a seasonal component and an irregular fluctuation, the forecasting is a combination of individual forecasting of each of these constituents. More precisely, the trend-cycle and the seasonal component are predicted with the help of fuzzy transform, pattern recognition and fuzzy natural logic techniques. To model the irregular component, we apply the Box–Jenkins approach. In the paper, we compare the suggested method with two other well-known methods, namely STL (see [14]) and ARIMA ones. [ABSTRACT FROM AUTHOR]

Highlights • A condition monitoring system based on non-destructive tests by ultrasonic waves was used to analyse wind turbine blades. • This paper employs an approach that considers advanced signal processing and machine learning to determine the thickness of the dirt and mud in a WTB. • Wavelet transform and Feature selection are employed to remove non-useful data. • Pattern recognition is carried out by Supervised Learning Classifiers based on statistical models to classify the signals. The results provided by PCA show an improvement over the AR results. • The novelty of this work is focused on applying this approach to detect and diagnose mud and dirt in WTB. Abstract Dirt and mud on wind turbine blades (WTB) reduce productivity and can generate stops and downtimes. A condition monitoring system based on non-destructive tests by ultrasonic waves was used to analyse it. This paper employs an approach that considers advanced signal processing and machine learning to determine the thickness of the dirt and mud in a WTB. Firstly, the signal is filtered by Wavelet transform. FE and Feature Selection(FS) are employed to remove non-useful data and redundant features. FS selects the number of the most significant terms of the model for fault detection and identification, reducing the dimension of the dataset. Pattern recognition is carried out by the following supervised learning classifiers based on statistical models to calculate and classify the signal depending on the fault: Ensemble Subspace Discriminant; k-Nearest Neighbours; Linear Support Vector Machine; Linear Discriminant Analysis; Decision Trees. Receiver Operating Characteristic analysis is used to evaluate the classifiers. Neighbourhood Component Analysis has been employed in feature selection. Several case studies of mud on the WTB surface have been considered to test and validate the approach. Autoregressive (AR) model and Principal Component Analysis (PCA) have been employed to FE. The results provided by PCA show an improvement on the AR results. The novelty of this work is focused on applying this approach to detect and diagnose mud and dirt in WTB. [ABSTRACT FROM AUTHOR]

The purpose of the present paper is to investigate a class of spherical functional autoregressive processes in order to introduce and study LASSO (Least Absolute Shrinkage and Selection Operator) type estimators for the corresponding autoregressive kernels, defined in the harmonic domain by means of their spectral decompositions. Some crucial properties for these estimators are proved, in particular, consistency and oracle inequalities. [ABSTRACT FROM AUTHOR]

This paper proposes a three-step method for detecting multiple structural breaks for piecewise stationary vector autoregressive processes. The number of structural breaks can be large and unknown with the locations of the breaks being different among different components. The proposed method is established via a link between a structural break problem and a high-dimensional regression problem. By means of this connection, a group orthogonal greedy algorithm, originated from the high-dimensional variable selection context, is developed for efficiently screening out potential break-points in the first step. A high-dimensional information criterion is proposed for consistent structural breaks estimation in the second step. In the third step, the information criterion further determines the specific components in which structural breaks occur. Monte Carlo experiments are conducted to demonstrate the finite sample performance, and applications to stock data are provided to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

This paper explores the spatial diffusion of Public-Private Partnership (PPP) in China, assessing the effects of regional boundaries, including political and functional boundaries, and transaction costs on this process. We conduct an empirical analysis using county-level panel data from 2014 to 2020 and employ a combination of geographic information system (GIS) and spatial autoregressive models to analyze the spatial factors influencing PPP diffusion in China. Our findings reveal three key points: First, local governments' PPP investment positively correlates with neighboring regions, indicating a contagious effect. Second, the contagious effect is more significant within the same urban agglomeration, demonstrating a boundary effect and illustrating the impact of spatial rescaling on spatial diffusion. Third, the contagious effect is more pronounced for PPP sectors with lower output measurement difficulty, suggesting transaction costs influence spatial diffusion. These findings offer valuable insights into barriers to spatial diffusion and strategies to promote the spatial diffusion of innovation. • We explore the spatial diffusion of Public-Private Partnership (PPP) in China. • We conduct an empirical analysis using county-level panel data from 2014 to 2020. • Results indicate a contagious effect in the spatial diffusion of PPP investment. • The contagious effect is more significant within the same urban agglomeration. • Transaction costs influence spatial diffusion. [ABSTRACT FROM AUTHOR]

In this paper a methodology to assess risk by forecasting the trend of marine losses at a global scale is presented. The proposed procedure, which can be used to continuously update an insurance company's costing model, identifies the most relevant risk indicators through Probabilistic Graphical Models (PGMs). The use of PGMs makes the variable selection more understandable since they provide a clear interface to interpret the model and perform predictions. Furthermore, this procedure can be used to verify independence relationships, validate the dataset and identify unexpected links among the considered variables. The robustness of estimates, crucial for risk assessment in the insurance context, is dealt with bootstrap. • Marine loss models are crucial for insurance companies in marine context • Probabilistic Graphical Models are useful to find relationships among variables • In this case study, we identified the most relevant 15 variables for marine losses • Among the five considered models, the GLARMA model showed the best performance [ABSTRACT FROM AUTHOR]

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations. • Detailed power and water operations are included in a hydrothermal scheduling model. • It incorporates short-term storage, reserve requirements, and irrigation effects. • The uncertain hydro inflows are represented by vector autoregressive models. • A calibration method captures the main effects induced by operational constraints. • Extensive experiments are analyzed on a detailed Chilean hydroelectric representation. [ABSTRACT FROM AUTHOR]

Composite Marginal Likelihood (CML) estimation and its advancements are popular ways to reduce the computational burden involved in the estimation of Multinomial Probit (MNP) models. CMLs use the product of marginal likelihoods of decision makers instead of the complete joint likelihood, reducing the numerical load. This allows for the estimation of models for larger and more complex data sets. The definition of the CML involves power weights on the marginal likelihoods that influence the statistical properties of the estimator. In this paper, we discuss how to effectively use the power weights in the cases of (1) unbalanced panel settings, where the weights help to reduce the variance of the estimator, and (2) unaccounted autoregressive structure of the errors, where the weights help to reduce the asymptotic bias of the estimator due to misspecification. • Derivation of variance optimal power weights for unbalanced panel MNP CML estimation • New minimal variance Two-Stage CML Estimator for unbalanced panel MNP models • Mitigation of misspecification effects through use of weights in MNP CML estimation [ABSTRACT FROM AUTHOR]

• A triple-gradient phononic acoustic black hole beam is proposed. • A comprehensive dynamic investigation on complex bandgap is conducted by using extended plane wave expansion method (EPWE) and hybrid dynamics method. • Akin to the traditional ABH effect, power-law gradients in density and modulus can also contribute significantly to broadband vibration attenuation. • Multi-objective optimization is adopted by a combination of NSGA-II and EPWE methods. Drawing on the principles of energy convergence and local resonance, the acoustic black hole (ABH) emerges as a promising unit cell within phononic crystals (PCs). It has the potential to achieve superior wave attenuation and lightweight performance simultaneously. In this paper, we propose a novel triple-gradient phononic ABH beam and strategically manipulate multiple gradients in the thickness, density, and modulus to enhance broadband vibration reduction. To characterize the dynamic performance, complex band and vibration transmission analyses are conducted by the extended plane wave expansion (EPWE) method and hybrid dynamics method (HDM), respectively. Via the comparative assessments of wave attenuation capacity and transmission loss between various multiple-gradient and single-gradient beam configurations, it reveals that the ABH effect is not solely brought by thickness gradient but also extends to the power-law gradients in density and modulus. More importantly, the synergistic development of three different gradient effects can lead to more pronounced and broader bandgaps in PCs. Meanwhile, a comprehensive parametric study on the bandgap characteristics reflects the trade-off relationship between structural strength and vibration attenuation capacity. Consequently, a multi-objective nonline ar optimization model that integrates the EPWE method and NSGA-II algorithm, is carried out to determine the optimal parameters of the proposed structure. The optimization outcome not only demonstrates the exceptionally wide bandgap performance of the optimized structure, but also serves as a valuable design guideline for ABH metamaterials geared towards broadband vibration reduction. [Display omitted] [ABSTRACT FROM AUTHOR]

• This paper examines freeway crash injury severity incorporating real-time weather data. • Spatial multinomial logit model was proposed to analyze freeway injury severity. • Risk factors such as vertical grade, visibility, EMS response time, and vehicle type are significant in the model. • Corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. Introduction: It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, "adverse weather," which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study. Methods: Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models. Result: The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade. [ABSTRACT FROM AUTHOR]

This paper presents NAR-RM, a method for learning robot reaching motions from a set of demonstrations using Nonlinear AutoRegressive (NAR) polynomial models. Reaching motions are modeled as solutions to autonomous discrete-time nonlinear dynamical systems, so that the movements started near the data of the demonstrations follow the trained trajectories and always reach and stop at the target. Since NAR models obtained using standard system identification techniques do not always adequately model the reaching motions, in this paper we present a method that uses a least-squares estimator with constraints to impose the location of fixed points in the model. With the imposition of new fixed points it is possible to change the location of the original fixed points of the model, thus allowing the learning of stable reaching motions. We evaluate our method using a library of human handwriting motions, a mobile robot and an industrial manipulator. [ABSTRACT FROM AUTHOR]

• Colored-noise Kalman filter is adopted in our system, which is more component to deal with the complex noise and alleviate the speech distortion. • A multi-objective DNN is first employed to joint estimate parameters of the clean speech autoregressive (AR) model and the noise AR model. Two kinds of DNN, i.e., fully-connected feed-forward network (FNN) and long short-term memory (LSTM), are adopted. • A post subtraction technique is employed to further remove the residual noise in the Kalman-filtered speech. • The proposed system takes advantage of both the DNN based method and Kalman filtering, and has a good generalization capability in both seen and unseen noise environments. In this paper, we propose a new speech enhancement system using a deep neural network (DNN)-augmented colored-noise Kalman filter. In our system, both clean speech and noise are modelled as autoregressive (AR) processes, whose parameters comprise the linear prediction coefficients (LPCs) and the driving noise variances. The LPCs are obtained through training a multi-objective DNN that learns the mapping from the noisy acoustic features to the line spectrum frequencies (LSFs), while the driving noise variances are obtained by solving an optimization problem aiming to minimize the difference between the modelled and observed AR spectra of the noisy speech. The colored-noise Kalman filter with DNN estimated parameters is then applied to the noisy speech for denoising. Finally, a post-subtraction technique is adopted to further remove the residual noise in the Kalman-filtered speech. Extensive computer simulations show that the proposed speech enhancement system achieves significant performance gains when compared to conventional Kalman filter based algorithms as well as recent DNN-based methods under both seen and unseen noise conditions. [ABSTRACT FROM AUTHOR]

In this paper, a multinomial autoregressive model for finite-range time series of counts is introduced to analyze the finite-range integer-valued data with more than two states. Basic probabilistic and statistical properties of the model are discussed. The related estimations of the parameters in proposed model are considered using conditional least squares (CLS), weighted conditional least squares (WCLS) and conditional maximum likelihood (CML) methods. The asymptotic properties of the estimators are established. Some simulation studies are conducted to verify the proposed procedure. A real example is analyzed to illustrate the advantages of our model. • A multinomial autoregressive model for finite-range time series of counts. • Basic probabilistic and statistical properties. • The CLS, WCLS and CML estimations and the related asymptotic properties. • A real data example is investigated to assess the performance. [ABSTRACT FROM AUTHOR]

• Heavy-tailedness is not formally considered for time series of counts. • The paper is the first attempt for modelling heavy-tailedness. • The proposed INAR model can capture other common features of dependent counts. • The considered model outperforms other models. • Numbers of cases, NSF fundings and transactions are analyzed, respectively. Count data frequently exhibit overdispersion, zero inflation and even heavy-tailedness (the tail probabilities are non-negligible or decrease very slowly) in practical applications. Many models have been proposed for modelling count data with overdispersion and zero inflation, but heavy-tailedness is less considered. The proposed model, a new integer-valued autoregressive process with generalized Poisson-inverse Gaussian innovations, is capable of capturing these features. The generalized Poisson-inverse Gaussian family is very flexible, which includes Poisson distribution, Poisson inverse Gaussian distribution, discrete stable distribution and so on. Stationarity and ergodicity of this model are investigated and the expressions of marginal mean and variance are provided. Conditional maximum likelihood is used for estimating the parameters, and consistency and asymptotic normality for the estimators are presented. Further, we consider the h -step forecast and diagnostics for the proposed model. The proposed model is applied to three real data examples. In the first example, we consider the monthly number of cases of Polio, which validates that the proposed model can take into account count data with excessive zeros. Then, we illustrate the use of the proposed model through an application to the numbers of National Science Foundation fundings. Finally, we apply the proposed model to the numbers of transactions in 5-min intervals for the stock traded at Empire District Electric Company. The second and third examples show that the proposed model has a good performance in modelling heavy-tailed count data. [ABSTRACT FROM AUTHOR]

This paper investigates the quasi-maximum likelihood estimator (QMLE) of the structure-changed and two-regime threshold double autoregressive model. It is shown that both the estimated threshold and change-point are n -consistent, and they converge weakly to the smallest minimizer of a compound Poisson process and the location of minima of a two-sided random walk, respectively. Other estimated parameters are n − consistent and asymptotically normal. The performance of the QMLE is assessed via simulation studies and a real example is given to illustrate our procedure. [ABSTRACT FROM AUTHOR]