Autoregressive Method Research Articles

Empirical evidence of the impact of mobility on property crimes during the first two waves of the COVID-19 pandemic

This paper seeks to evaluate the impact of the removal of restrictions (partial and complete) imposed during COVID-19-induced lockdowns on property offences such as robbery, burglary, and theft during the milder wave one and the more severe wave two of the pandemic in 2020 and 2021, respectively. Using 10-year data of the daily counts of crimes, the authors adopt an auto-regressive neural networks method to make counterfactual predictions of crimes, representing a scenario without the pandemic-induced lockdowns. The difference between the actual and forecast is the causal impact of the lockdown in all phases. Further, the research uses Google Mobility Community Reports to measure mobility. The analysis has been done at two levels: first, for the state of Tamil Nadu, which has a sizeable rural landscape, and second for Chennai, the largest metropolitan city with an urban populace. During the pandemic-induced lockdown in wave one, there was a steep decline in the incidence of property offences. On removing restrictions, the cases soared above the counterfactual predicted counts. In wave two, despite the higher severity and fatality in the COVID-19 pandemic, a similar trend of fall and rise in property cases was observed. However, the drop in mobility was less substantial, and the increase in the magnitude of property offences was more significant in wave two than in wave one. The overall trend of fluctuations is related to mobility during various phases of restrictions in the pandemic. When most curbs were removed, there was a surge in robberies in Tamil Nadu and Chennai after adjusting for mobility. This trend highlights the effective increase in crime due to pandemic-related economic and social consequences. Further, the research enables law enforcement to strengthen preventive crime work in similar situations, when most curbs are removed after a pandemic or other unanticipated scenarios.

CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing

The text-based speech editor allows the editing of speech through intuitive cutting, copying, and pasting operations to speed up the process of editing speech. However, the major drawback of current systems is that edited speech often sounds unnatural due to cut-copy-paste operation. In addition, it is not obvious how to synthesize records according to a new word not appearing in the transcript, which often needs the help of text-to-speech (TTS) and voice conversion (VC) technology at the same time. This paper first proposes a novel end-to-end text-based speech editing method called context-aware mask prediction network (CampNet). The model can simulate the text-based speech editing process by randomly masking part of speech and then predicting the masked region by sensing the speech context. It can solve unnatural prosody in the edited region and synthesize the speech corresponding to the unseen words in the transcript. Secondly, for the possible operation of text-based speech editing, we design three text-based operations based on CampNet: deletion, insertion, and replacement. These operations can cover various situations of speech editing. Thirdly, to synthesize the speech corresponding to long text in insertion and replacement operations, a word-level autoregressive generation method is proposed, which can synthesize the speech of arbitrary length text. Fourthly, we propose a speaker adaptation method using only one sentence for CampNet and explore the ability of few-shot learning based on CampNet, which provides a new idea for speech forgery tasks. The subjective and objective experiments<xref ref-type="fn" rid="fn1">¹</xref><fn id="fn1"><label>¹</label> Examples of generated speech can be found at <uri>https://hairuo55.github.io/CampNet</uri>. </fn> on VCTK and LibriTTS datasets show that the speech editing results based on CampNet are better than TTS technology, manual editing, and VoCo method (the combination of TTS and VC). We also conduct detailed ablation experiments to explore the effect of the CampNet structure on its performance. Finally, the experiment shows that speaker adaptation with only one sentence can further improve the naturalness of speech editing for one-shot learning.

Analysis of the Role of Promoting Sustainable Green Growth through Government Agencies in a Legal Context.

The rapid development of the global economy has also caused the deterioration of the natural environment. The traditional economic growth method can no longer meet the development requirements of the times for the environment. The realization of sustainable and green development of human society has become the consensus of the whole society. To improve the degree of the greening of the economy, it is necessary to coordinate the management of various departments and to integrate the concept of environment and resources into economic growth. It is an important way for government departments to formulate corresponding sustainable green growth policies by relying on laws to achieve the strategic goals of sustainable green growth. In order to explore the role of government agencies in sustainable green growth through the enactment of relevant laws, this article takes China as the study of the environment. From the perspective of environmental protection tax law, through the establishment of the relationship model between ET and economic variables and ET and environmental indicators, using vector autoregression and system dynamics simulation analysis methods, the effect of ET on the environment and economy is studied. In the analysis of the impact on the environment, the impact of the auxiliary policy of ET on sustainable green growth is discussed. The experimental results show that the impact of environmental tax (ET) on GDP is more obvious. The simulation results show that when the proportion of technology investment is 35%, the promotion effect of sustainable green growth is the best. That is to say, the formulation of relevant laws and policies by government agencies has a certain role in promoting sustainable green growth.

Relationship between Urban Innovation Capability and Energy Utilization Efficiency: An Empirical Study of 281 Prefecture‐Level Cities in China

Following a dynamic nonlinear perspective, this study explores the relationship between urban innovation capability and energy utilization efficiency by employing the Panel Vector Autoregression (PVAR) and Dynamic Panel Threshold Regression (DPTR) methods. Using the 2003–2020 panel data of 281 prefecture‐level cities in China, this study confirms that energy utilization efficiency improves owing to the improvement of urban innovation capability. Depending on the characteristics of the city, such as population density, industrial structure, and environmental pollution, high energy utilization efficiency in the early stages of city development may help or hinder the improvement of energy utilization efficiency in the later stages. The enhancement in urban innovation capability has failed to improve energy utilization efficiency and has adversely affected cities with a low population density or weak secondary industrial foundation. However, in cities with a high population density or proportion of secondary industry, the improvement in innovation capability significantly increases the efficiency of energy utilization. In addition, the positive effect that urban innovation capability has on energy utilization efficiency is higher in low‐pollution cities than in high‐pollution cities.

PENGARUH PERGERAKAN RETURN BURSA SAHAM NEGARA ASEAN: TINJAUAN FENOMENA SPILLOVER ATAUKAH DAMPAK NILAI TUKAR?

In March 2018, the United States announced a policy regarding import duties which contained restrictions on incoming goods from the People's Republic of China and early 2020, the whole world was faced with the COVID-19 pandemic causing many changes to macroeconomic indicators in many countries, including the stock market. This study uses historical data on the stock markets of the Americas, Indonesia, Singapore, Malaysia, the Philippines and Vietnam and the exchange rates of the Indonesian, Singaporean, Malaysian, Philippine and Vietnamese currencies against the USD to test whether there is a significant influence between the returns on the American stock market and the exchange rates. money of each country on stock market returns in Indonesia, Singapore, Malaysia, the Philippines and Vietnam. The author analyzes stock market returns using the Threshold GARCH method and continues by using the Standard GARCH method for heteroscedastic stock market returns and the Vector Autoregressive (VAR) method for non-heteroscedastic stock market returns. The results of the study show that the return on the American stock market and the exchange rates of currencies of ASEAN countries against the USD are related to the stock market returns of Indonesia, Singapore, Malaysia, the Philippines and Vietnam.

Research on Low Resource Neural Machine Translation Based on Non-autoregressive Model

The autoregressive model can’t make full use of context information because of its single direction of generation, and the autoregressive method can’t perform parallel computation in decoding, which affects the efficiency of translation generation. Therefore, we explore a non-autoregressive translation generation method based on insertion and deletion in low-resource languages, which decomposes translation generation into three steps: deletion-insertion-generation. Therefore, the dynamic editing of the translation can be realized in the iterative updating process. At the same time, each step can be calculated in parallel, which improves the decoding efficiency. In order to reduce the complexity of data sets in non-autoregressive model training, we have trained Uyghur-Chinese training data with sequence-level knowledge distillation. Experiments on Uyghur-Chinese, English-Romanian distilled data sets and standard data sets verify the effectiveness of the non-autoregressive method.

Non-Autoregressive ASR Modeling Using Pre-Trained Language Models for Chinese Speech Recognition

Transformer-based models have led to significant innovation in various classic and practical subjects, including speech processing, natural language processing, and computer vision. On top of the Transformer, attention-based end-to-end automatic speech recognition (ASR) models have become a popular fashion in recent years. Specifically, an emergent research topic is non-autoregressive modeling, which can achieve fast inference speed and obtain competitive performance when compared with conventional autoregressive methods. In addition, in the context of natural language processing, the bidirectional encoder representations from Transformers (BERT) model and its variants have received widespread attention, partially due to their ability to infer contextualized word representations and obtain superior performances of downstream tasks through simple fine-tuning. However, to our knowledge, leveraging the synergistic power of non-autoregressive modeling and pre-trained language model for ASR remains relatively underexplored. In this regard, this study presents a novel pre-trained language model-based non-autoregressive ASR framework. A series of experiments were conducted on two publicly available Chinese datasets, AISHELL-1 and AISHELL-2, to demonstrate competitive or superior results of the proposed ASR models when compared with well-practiced baseline systems. In addition, a set of comparative experiments is likewise carried out with different settings to analyze the performance of the proposed framework.

Measuring the Impact of Oil Revenues on the Level of Public Expenditures for the Iraqi Economy for the Period from (2004-2019)

Oil-producing countries depend on oil revenues mainly to finance their public budgets and thus influence Directly on the public spending process and according to economic logic, the financial abundance leads to an increase in public spending. And that's what A detailed event in the Iraqi economy during the years of explosive budgets for the years 2012 and 2013 due to high prices Oil has reached unprecedented levels. The process of public spending did not focus on the investment side, which would The economy and the various sectors developed, as they focused only on recreational and consumer activities, which led to the loss of an opportunity Financial abundance and the lack of impediment to the advancement of the Iraqi economy. The research focuses on the variables of oil revenues and public spending and the close relationship that exists between them. The quarterly data was taken, which is more accurate than the annual data, and the sample consisted of 66. Watching from 2004 to 2019. The series was subjected to silence tests for the two mentioned variables, and it was found that they did not They settle at the level and are stabilized when taking the first difference and after doing the graph test and proceeding to complete the rest the exams . An autoregressive ARDL method was selected, and the appropriate model was selected. For each of the oil revenues and public spending, and regarding the integration parameter, it was negative and significant, as well as the limits test that He indicated the possibility of long-term interpretation that the coefficients of the variables were significant, as the percentages of significance ranged between 1%, 5%, and . 10% of the sequence. The model was subjected to a dimensional test, which confirmed the integrity of the model from standard problems and the statistical as a test of autocorrelation of the residuals and the result was not significant. As well as the test for instability of homogeneity of variance, which is The other is immaterial.

Technological progress spillover effect in Lithuanian manufacturing industry

Research background: Various methods for technological progress assessment and evaluation exist in the context of economic development. Each of the methods possesses distinct advantages and disadvantages in analysis of technological progress fluctuations. For most neoclassical growth theories, technological progress measures are included as exogenous variables, thus excluding evaluation of factors influencing technological progress variation throughout time.&#x0D; Purpose of the article: The aim of this article is to offer improvements on classical technological progress evaluation methodologies for manufacturing industries, separating effect of intersectoral technological progress spillover effect from internal factors influencing technological progress growth and perform analysis in the case of Lithuanian manufacturing industry.&#x0D; Methods: Earlier research papers used linear time series regression and vector autoregression methods to assess technological progress values and define equations explaining effect of different manufacturing level indicators on technological progress measure growth. This research paper uses results of previously mentioned methods and performs simulation analysis applying agent-based modelling framework.&#x0D; Findings &amp; value added: The conducted vector autoregression analysis has showed that two variables which influence technological progress most significantly are labor productivity measure and gross profit value. Sensitivity analysis emphasizes that effect of these two variables on technological progress growth is substantially different. Increase in gross profit value affects technological progress growth for wider range of sectors from Lithuanian manufacturing industry (15 out of 18 analyzed sectors? technological progress measure values are affected by changes in gross profit, while changes in labor productivity influence technological progress values in the case of 9 sectors). Rising gross profit values also produce intersectoral technological progress spillover effect more significantly, while growth in labor productivity measure has stronger effect on technological progress fluctuations for sectors which are able to exploit this effect. Presented research suggests improved methodology for intersectoral technological progress spillover effect assessment in the context of manufacturing industries.

Application of the Spatial Autoregressive (SAR) Method in Analyzing Poverty in Indonesia and the Self Organizing Map (SOM) Method in Grouping Provinces Based on Factors Affecting Poverty

The economy is a benchmark to determine the extent of the development of a country. Indonesia, which is now a developing country, is ranked 5th as the poorest country in Southeast Asia. Of course, the government must pay attention because until now, poverty has become one of Indonesia's main problems. Ending poverty everywhere and in all its forms is goal 01 of the Sustainable Development Goals (SDGs) program. One of the efforts that can be done is by planning as part of the implementation of the target, namely eliminating poverty and appropriate social protection for all levels of society so that the SDGs are achieved. Therefore, it is important to do a spatial analysis by making a model of poverty estimation in Indonesia and grouping to identify areas in Indonesia that have the highest poverty mission. The clustering method used in this grouping is Self Organizing Map (SOM). In this study, Spatial Autoregressive (SAR) analysis was used to create a predictive model. This is because poverty is very likely to have a spatial influence or be influenced by location to other areas in the vicinity. The results of the SAR model that can be formed are . Furthermore, the region with the highest mission is grouped using the Self Organizing Map (SOM) clustering based on variables that significantly affect the amount of poverty in Indonesia. From the results of the analysis obtained four clusters, each of which has its characteristics to classify 34 provinces in Indonesia. The clusters formed include cluster 1 consisting of 17 provinces, cluster 2 consisting of 9 provinces, cluster 3 consisting of 1 province, and cluster 4 consisting of 7 provinces.

Infrastructures Development, Environmental Quality and Economic Growth in Nigeria

The Earth as a planet supports human life, living and activities, attracting extensive and intensive socioeconomic influences on the economy. Such activities like infrastructures development exerts increasing and divers environmental quality concerns and hence the economic growth. While these variables appear interrelated due to many factors including population growth, urbanisation, etc. However, the relationship between infrastructures, environment and economic growth is not largely known especially in Nigeria. This study therefore investigated their relationship using time series data between 1990 and 2019 by adopting Co-integration estimation technique through the Bound test approach of auto regressive distributive lag method using percentage share of building and construction sector of gross domestic product (GDP), carbon dioxide, population growth GDP growth rate, etc. as variables. The study revealed that the infrastructures development and environmental quality explain economic growth and have both short and long run relationships while specifically population growth and agriculture, forestry, fishing, value added variables are positively significant to economic growth. The findings evidences of both short and long run relationships among the variables are significant and it is consequently recommended that new roles for infrastructure sets and production processes should consider environmental quality mindsets to achieve positive green economy outcomes in Nigeria. JEL Classifications: O18, O44, Q5

Інформаційна технологія для прогнозування часових рядів методом синтезу прогнозної схеми

The study is devoted to the development of information technology for forecasting based on time series. It has been found that it is important to develop new models and forecasting methods to improve the quality of the forecast. Information technology is based on the evolutionary method of synthesis of the forecast scheme grounded on basic forecast models. The selected method allows you to consider any number of predictive models that may belong to different classes. For a given time series, the weight coefficients with which the models are included in the resulting forecast scheme are calculated by finding the solution to the optimization problem. The method of constructing the objective function for the optimization problem in the form of a linear combination of forecasting results by basic forecasting models is shown. It is proposed to find the solution to the optimization problem using a genetic algorithm. The result of the method is the forecast scheme, which is a linear combination of basic forecast models. To assess the quality of the forecast, it is suggested to use forecasting errors or forecast volatility calculated as the standard deviation. Forecast quality criteria are selected depending on the context of the task. The use of forecast volatility as a quality criterion, with repeated use of technology, will reduce the deviation of forecast values from real data. The structural scheme of information technology is developed. Structurally, information technology consists of two blocks: data processing and interpretation of the obtained values. The result of the application of the developed information technology is the production rules for determining the predicted value of the studied quantity. Experimental verification of the obtained results was performed. The problem of forecasting the number of religious organizations in Ukraine based on statistical data from 1997 to 2000 has been solved. The autoregression method and the linear regression model were chosen as the basic forecast models. Based on the results of using the developed information technology, the weights of the basic models were calculated. It is demonstrated that the obtained forecast scheme allowed to improve the average absolute percentage error and forecast volatility in comparison with the selected models. Keywords: information technology; time series; forecasting; evolutionary technologies; forecast volatility; synthesis of the forecast scheme.

Risk measurement for conditionally heteroscedastic location-scale time series models with ASTD and AEPD innovations

This study examines the asymptotic properties of a class of conditionally heteroscedastic location-scale time series models with innovations following a generalized asymmetric Student-t distribution (ASTD) or an asymmetric exponential power distribution (AEPD). We first show the consistency and asymptotic normality of the conditional maximum likelihood estimator of the model parameters under certain regularity conditions. Then, based on the maximum likelihood estimator, we estimate conditional value-at-risk (VaR) and expected shortfall (ES) by using their closed forms induced from the model. Their performance is finally compared with that of conditional autoregressive VaR and expectile methods. A simulation study and real data analysis are provided as an illustration.

Autoregressive spectrum-guided variational mode decomposition for time-varying modal identification under nonstationary conditions

Modal identification is an important research subject in structural health monitoring. It is a mature subject for modal identification of time-invariant and stationary conditions. Because time-varying properties are valuable for damage detection and time-varying system identification, and because the environmental loads do not always satisfy the assumption of stationarity, it is necessary to develop accurate identification methods for time-varying structures, structures under nonstationary excitations, or the coupling of the two conditions. The variational mode decomposition (VMD) method, which adaptively decomposes the signal into narrow-band intrinsic modes, shows the potential for time-varying and nonstationary conditions. However, parameter selection greatly affects the accuracy of mode decomposition. In this study, a new modal identification method based on an autoregressive spectrum-guided variational mode decomposition method is proposed for the application of time-varying and nonstationary conditions. First, the determination of the initial center frequencies for VMD is proposed using an autoregressive spectrum-guided method to greatly improve the accuracy. Second, a method to select the optimal balancing factor is developed to improve the decomposition accuracy by controlling the proper bandwidth. Subsequently, an instantaneous frequency identification method is presented by detecting the ridge of the time–frequency distribution. Finally, studies of numerical and actual structures demonstrate the capacity of the proposed method for practical applications of time-varying cases and nonstationary conditions.

A state-informed stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter

Objective. We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for state-informed sensory stimulation in electroencephalography (EEG) experiments. Approach. The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation. Main results. Our method showed higher accuracy in predicting the EEG phase than the conventional autoregressive (AR) model-based method. Significance. A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated AR model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.

Session centered Recommendation Utilizing Future Contexts in Social Media

Abstract Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the objective interaction are present and this supplies indispensable signal on preferences of users and if utilized can increase the quality of recommendation. It is a subtle task to incorporate future contexts into the process of training, as the rules of machine learning are not followed and can result in loss of data. Therefore, in order to solve this problem, we suggest a novel encoder decoder prototype termed as space filling centered Recommender (SRec), which is used to train the encoder and decoder utilizing space filling approach. Particularly, an incomplete sequence is taken into consideration by the encoder as input (few items are absent) and then decoder is used to predict these items which are absent initially based on the encoded interpretation. The general SRec prototype is instantiated by us employing convolutional neural network (CNN) by giving emphasis on both e ciency and accuracy. The empirical studies and investigation on two real world datasets are conducted by us including short, medium and long sequences, which exhibits that SRec performs better than traditional sequential recommendation approaches.

A novel auto-regressive dynamic slow feature analysis method for dynamic chemical process monitoring

A novel dynamic data modeling method called autoregressive dynamic slow feature analysis (RDSFA) is proposed for dynamic process concurrent monitoring of operating condition deviations and process dynamics anomalies. In this method, a multi-goal optimization question is formulized with constraints of the extract latent variable catch some variation information and mutually orthogonal to extract a group of latent features which change slowly over time and whose autoregressive model are built explicitly. When the proposed method is applied to the process monitoring, in addition to the statistics about slow feature and whose first derivatives with respect to time which have existed in SFA based monitoring frame, we add other two statistics which only contain unpredictable variability information to provide more sensitive monitoring results. Case studies on simulation data, data from a CSTR process are presented to reveal the efficiency and the superiority of the proposed method as compared to other related methods.

Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series.

The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extensive evaluation study is missing. In this work we consider some of the best-known causality measures i.e., cross-correlation, (conditional) Granger causality index (CGCI), partial directed coherence (PDC), directed transfer function (DTF), and partial mutual information on mixed embedding (PMIME). To correct for noise-related spurious connections, each measure (except PMIME) is tested for statistical significance based on surrogate data. The performance of the causality metrics is evaluated on a set of simulation models with distinct characteristics, to assess how well they work in- as well as outside of their “comfort zone.” PDC and DTF perform best on systems with frequency-specific connections, while PMIME is the only one able to detect non-linear interactions. The varying performance depending on the system characteristics warrants the use of multiple measures and comparing their results to avoid errors. Furthermore, lags between coupled variables are inherent to real-world systems and could hold essential information on the network dynamics. They are however often not taken into account and we lack proper tools to estimate them. We propose three new methods for lag estimation in multivariate time series, based on autoregressive modelling and information theory. One of the autoregressive methods and the one based on information theory were able to reliably identify the correct lag value in different simulated systems. However, only the latter was able to maintain its performance in the case of non-linear interactions. As a clinical application, the same methods are also applied on an intracranial recording of an epileptic seizure. The combined knowledge from the causality measures and insights from the simulations, on how these measures perform under different circumstances and when to use which one, allow us to recreate a plausible network of the seizure propagation that supports previous observations of desynchronisation and synchronisation during seizure progression. The lag estimation results show absence of a relationship between connectivity strength and estimated lag values, which contradicts the line of thinking in connectivity shaped by the neuron doctrine.

Will COVID-19 Threaten the Survival of the Airline Industry?

The pandemic causes social distancing and lockdown, which impedes consumer confidence and contracts the economy. Hence, this study analyzes the corona (COVID-19) impact on the airline industry revenues (ALR) and forecast by the vector autoregression (VAR) method. The results indicate that gross domestic product (GDP) and air cargo are the best predictors of ALR. The forecasting outcomes explore if ALR will decline and expect to back to pre-COVID-19 in 2023. Our results resemble both the V-shaped and U-shaped, which suggests slow gradual recovery with longer lockdown and border disclosure. The government can restore confidence building by providing economic stimulus packages and can encourage the airline to return to travel. Furthermore, softening the passenger rules concerning the refund of unflown ticket, reducing taxes, and reducing overflight taxes, all reduce the costs. Similarly, the mutually recognized global standards are crucial for effective execution, and any temporary measures taken by the government should have a clear exit strategy. The study major limitation includes the lack of relevant research and data availability.

A Method for More Accurate Determination of Resonance Frequency of the Cardiovascular System, and Evaluation of a Program to Perform It.

This study validated a more exact automated method of determining cardiovascular resonance frequency (RF) against the "stepped" protocol described by Lehrer et al. (Appl Psychophysiol Biofeedback 25(3):177-191, https://doi.org/10.1023/a:1009554825745 , 2000; in Foundations of heart rate variability biofeedback: A book of readings, The Association for Applied Psychophysiology and Biofeedback, pp 9-19, 2016). Thirteen participants completed a 15-min RF determination session by each method.The "stepped" protocol assesses HRV in five 3-min stationary windows from 4.5 to 6.5 breaths per minute (bpm), decreasing in 0.5bpm steps. Multiple criteria, subjectively weighted by the clinician, determines RF. For this study, the proposed method used a sliding window with a fixed rate of change (67.04ms per breath) at each of 78 breath cycles ranging from 4.25 to 6.75bpm. Its algorithm analyzes IBI to locate the midpoint of the 1-min region of stable maximum peak-trough variability. RF is quantified from breath duration at that point. The software generates a visual display of superimposed HR and breathing data. Thus, the new method fully automates RF determination. Eleven of the 13 matched pairs fell within the 0.5bpm resolution of the stepped method. Comparisons of LF power generated by the autoregressive (AR) spectral method showed a strong correlation in LF power production by the stepped and sliding methods (R = 0.751, p = 0.000). The "sliding" pacing protocol was favored by 69% of participants (p < 0.02). The new, fully-automated, method may facilitate both in-person and remote HRV biofeedback training. Software is available open-source.