Autoregressive Method Research Articles

Deep learning for the spatial additive autoregressive model with nonparametric endogenous effect

The success of deep learning has inspired recent interests in applying neural networks in statistical inference. In this paper, we investigate the use of deep neural networks for additive spatial autoregressive model with nonparametric endogenous effect. We construct the spatial autoregressive neural network (SARNN) to characterize spatial effects and fit the model. Our method is naturally applicable to linear or partially linear cases. Compared with other general spatial autoregression methods, our approach is competitive. Unlike normal black box neural networks, the architecture of SARNN allows itself to be interpretable with respect to each covariates. Compared with the spline method of nonparametric spatial autoregression, our method is more effective for different nonparametric functions and different distributions of spatial data. Extensive simulation studies and analysis of real data set highlights the usefulness of the proposed model and method.

Can a Novel Natural Language Processing Model and Artificial Intelligence Automatically Generate Billing Codes From Spine Surgical Operative Notes?

Retrospective cohort. Billing and coding-related administrative tasks are a major source of healthcare expenditure in the United States. We aim to show that a second-iteration Natural Language Processing (NLP) machine learning algorithm, XLNet, can automate the generation of CPT codes from operative notes in ACDF, PCDF, and CDA procedures. We collected 922 operative notes from patients who underwent ACDF, PCDF, or CDA from 2015 to 2020 and included CPT codes generated by the billing code department. We trained XLNet, a generalized autoregressive pretraining method, on this dataset and tested its performance by calculating AUROC and AUPRC. The performance of the model approached human accuracy. Trial 1 (ACDF) achieved an AUROC of .82 (range: .48-.93), an AUPRC of .81 (range: .45-.97), and class-by-class accuracy of 77% (range: 34%-91%); trial 2 (PCDF) achieved an AUROC of .83 (.44-.94), an AUPRC of .70 (.45-.96), and class-by-class accuracy of 71% (42%-93%); trial 3 (ACDF and CDA) achieved an AUROC of .95 (.68-.99), an AUPRC of .91 (.56-.98), and class-by-class accuracy of 87% (63%-99%); trial 4 (ACDF, PCDF, CDA) achieved an AUROC of .95 (.76-.99), an AUPRC of .84 (.49-.99), and class-by-class accuracy of 88% (70%-99%). We show that the XLNet model can be successfully applied to orthopedic surgeon's operative notes to generate CPT billing codes. As NLP models as a whole continue to improve, billing can be greatly augmented with artificial intelligence assisted generation of CPT billing codes which will help minimize error and promote standardization in the process.

Influence of green financing, technology innovation, and trade openness on consumption-based carbon emissions in BRICS countries

The study explores the dynamic effects of renewable energy investment (green financing), green technology, and trade openness on consumption-based (trade-adjusted) carbon emissions in BRICS economies from 2000 to 2020. The study employs the cross-section autoregressive distributed lag method for empirical estimation to address slope heterogeneity and cross-sectional dependency issues in panel data. The findings exhibit that green financing and sustainable technologies mitigate consumption-based carbon emissions in the long-run, while trade openness contributes to emissions in BRICS countries. The short-run outcomes are compatible with long-run; however, the magnitude of long-run estimates is larger than the short-run. Moreover, the error correction term reveals a significant negative coefficient value, endorsing the conversion towards steady-state equilibrium with a 37% yearly adjustment rate in case of any deviation from equilibrium. The robustness of results is confirmed through augmented mean group and common correlated effect mean group. These findings imply that BRICS countries should encourage financing in renewable energy projects and allocate R&D investment to promote the adaptation of sustainable technologies. In addition, sustainable and green trade policies would help to curb trade-adjusted pollution.

Does environment quality affect the health care spending? Nexus among CO2 emissions, non-renewable energy production, financial development, and health care spending.

This study investigates the dynamic associations among carbon dioxide (CO2) emissions, non-renewable energy production from petroleum derivatives, financial development, and healthcare expenditures to improve environmental quality. This research has employed the balanced annual panel of thirty (30) Organizations for Economic Co-operation and Development (OECD) countries' data set and applied panel vector autoregression (VAR) method depending on the generalized method of moments (GMM). Furthermore, the empirical findings reveal that health spending and CO2 emissions have a favorable bidirectional link, but there is no indication that health spending promotes power generation. The results demonstrate that increased energy consumption and productions affect pollution, and higher CO2 emissions increase healthcare costs. Whereas, energy consumption, financial development, and healthcare expenditures have a positive toward environmental quality.

The influence of membership fluidity on the coevolution of the social and knowledge systems in online knowledge communities

PurposeIn the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.Design/methodology/approachBased on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.FindingsBy analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.Originality/valueThis paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

A Regularized K-means Autoregressive Shrinkage Method to Portfolio Optimization

Estimation is always an issue of portfolio optimization. To address this issue this paper started from addressing global minimum variance (GMV) portfolio, which gives the weights as the function of estimated covariance matrix of the asset net returns. The inverse operation of the matrix always leads to ill-conditioned matrix due to the noise in the return series. Therefore, regularized sample covariance matrix is often used to address this problem. In this work, a regularized autoregressive shrinkage method for covariance matrix estimation with the historical data is developed, which regards the future covariance matrix as the function of the historical covariance matrix. K-means clustering algorithm is also added to enhance the performance of the regularized autoregressive model. The results demonstrate that the performance of GMV portfolio can be enhanced with regularization under autoregressive shrinkage model.

A novel framework for the removal of pacing artifacts from bio-electrical recordings

BackgroundElectroceuticals provide clinical solutions for a range of disorders including Parkinson's disease, cardiac arrythmias and are emerging as a potential treatment option for gastrointestinal disorders. However, pre-clinical investigations are challenged by the large stimulation artifacts registered in bio-electrical recordings. MethodA generalized framework capable of isolating and suppressing stimulation artifacts with minimal intervention was developed. Stimulation artifacts with different pulse-parameters in synthetic and experimental cardiac and gastrointestinal signals were detected using a Hampel filter and reconstructed using 3 methods: i) autoregression, ii) weighted mean, and iii) linear interpolation. ResultsSynthetic stimulation artifacts with amplitudes of 2 mV and 4 mV and pulse-widths of 50 ms, 100 ms, and 200 ms were successfully isolated and the artifact window size remained uninfluenced by the pulse-amplitude, but was influenced by pulse-width (e.g., the autoregression method resulted in an identical Root Mean Square Error (RMSE) of 1.64 mV for artifacts with 200 ms pulse-width and both 2 mV and 4 mV amplitudes). The performance of autoregression (RMSE = 1.45 ± 0.16 mV) and linear interpolation (RMSE = 1.22 ± 0.14 mV) methods were comparable and better than weighted mean (RMSE = 5.54 ± 0.56 mV) for synthetic data. However, for experimental recordings, artifact removal by autoregression was superior to both linear interpolation and weighted mean approaches in gastric, small intestinal and cardiac recordings. ConclusionsA novel signal processing framework enabled efficient analysis of bio-electrical recordings with stimulation artifacts. This will allow the bio-electrical events induced by stimulation protocols to be efficiently and systematically evaluated, resulting in improved stimulation therapies.

Short-term probabilistic prediction of significant wave height using bayesian model averaging: Case study of chabahar port, Iran

Accurate predictions of significant wave heights are important for a number of maritime applications, such as design of coastal and offshore structures. In the present study, an ensemble approach of Bayesian model averaging (BMA) is used for the prediction of significant wave heights. The BMA is used in conjunction with three machine learning methods: Multivariate Adaptive Regression Spline (MARS), Random Forest (RF) and Gradient Boosted Regression Trees (GBRT). Daily data from three wave monitoring stations located near the Chabahar Bay in the Gulf of Oman are used to evaluate performance of the methods in predicting the significant wave height for the lead times of one, two, three and six days. The machine learning methods are also compared with auto-regressive method. The obtained results indicate that BMA model performs slightly better than the GBRT, MARS, RF and AR methods in some cases especially for the six-day ahead time horizon and all the methods are generally competitive in predicting significant wave heights.

An Unsupervised Learning Approach for Wayside Train Wheel Flat Detection.

One of the most common types of wheel damage is flats which can cause high maintenance costs and enhance the probability of failure and damage to the track components. This study aims to compare the performance of four feature extraction methods, namely, auto-regressive (AR), auto-regressive exogenous (ARX), principal component analysis (PCA), and continuous wavelet transform (CWT) capable of automatically distinguishing a defective wheel from a healthy one. The rail acceleration for the passage of freight vehicles is used as a reference measurement to perform this study which comprises four steps: (i) feature extraction from acquired responses using the specific feature extraction methods; (ii) feature normalization based on a latent variable method; (iii) data fusion to enhance the sensitivity to recognize defective wheels; and (iv) damage detection by performing an outlier analysis. The results of this research show that AR and ARX extraction methods are more efficient techniques than CWT and PCA for wheel flat damage detection. Furthermore, in almost every feature, a single sensor on the rail is sufficient to identify a defective wheel. Additionally, AR and ARX methods demonstrated the potential to distinguish a defective wheel on the left and right sides. Lastly, the ARX method demonstrated robustness to detect the wheel flat with accelerometers placed only in the sleepers.

Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network

In this study we advance the understanding of the spillovers and connectedness network among conventional and Islamic BRICS stock markets, cryptos (Bitcoin, Ethereum, Litecoin) and various global uncertainties, using a quantile vector autoregression method and daily data covering the period October 8, 2016, to May 28, 2021. Further, the study uses a network and sensitivity analyses to assess the nexus, examines risk causes, and the transfer paths in these markets under bearish, normal, and bullish markets. The evidence offers major findings. First, the overall static and dynamic connectedness is very high and more intense at extreme events. Second, the network connectedness structure shows that the markets have played both roles: net transmitters and receivers of shocks under several market states. Finally, the sensitivity to quantiles analysis shows switching behavior of net transfer spillovers over the quantiles. This could be beneficial to investors aiming at optimizing hedging strategies. Policymakers should consider carefully the overall network connectedness in the market system and formulate appropriate policies to conceive stock market price sensitivity.

Effect of exchange rate on inflation in the inflation targeting framework: Is the threshold level relevant?

The continuous depreciation of the exchange rate in Ghana has raised concern about its effect on inflation and the economy at large. This paper examines the threshold effect of exchange rate pass-through (ERPT) on inflation using a monthly data from January 2002 to December 2018. The relevance of the exchange rate threshold in the Taylor rule has also been examined. Using the threshold autoregressive (TAR) method, the results of the ERPT model revealed that exchange rate depreciation beyond a monthly threshold of 0.70% has a significant positive pass-through effect on inflation, which gives credence to the relevance of threshold level. The results of the monetary policy rule model also showed that regardless of the threshold level of 0.51%, the exchange rate significantly influences the monetary policy rate positively. Therefore, paying proper attention to the exchange rate in the policy rule despite the threshold (0.51%) will prevent the exchange rate depreciation from exceeding the optimal level (0.70%) and hence no ERPT on inflation. Based on these findings, it is suggested that monetary regulators should view the exchange rate at any level essential to adjust the policy rate.

Prediction of Covid-19 Cases in Central Java using the Autoregressive (AR) Method

Since the beginning of the Covid-19 case in Indonesia in March 2020, more than 6 million confirmed cases had been confirmed. The rapid development of this case can be accessed through the covid19.go.id page. In Central Java province, confirmed cases as of July 6, 2022, reached 628,393 people, with the number of recovered patients reaching 594,783 people and the number of patients dying as many as 33,215 people. With this data, a prediction is needed to help the government anticipate an increase in Covid-19 cases in Central Java Province. This study aims to create a forecasting model using the Autoregressive (AR) method by optimizing the function parameters. Then Mean Squared Error (MSE) to analyze the results of forecasting data errors. The results are the best parameter functions on AR (30) with the smallest MSE. Furthermore, predictions are made from July 1 to August 30, 2022, showing an increase in cases

Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio

The present study is a pure experimental investigation of the viscosity and rheological properties of the Al2O3-Fe2O3 hybrid nanofluid and the development of a new correlation. The main purpose of the study is to evaluate the effect of the Al2O3-Fe2O3 mixture ratio on the viscosity property and develop a correlation for the viscosity prediction. The Al2O3 and Fe2O3 were first characterized using XRD diffraction and the FESEM technique. The nanofluid was prepared using a two-step method using base fluid consisting of water and ethylene glycol mixture at 60/40 ratios. Five different Al2O3-Fe2O3 nanoparticle compositions were investigated experimentally for the viscosity and rheological properties at temperatures between 0 and 100 °C. The experimental data shows that the Al2O3-Fe2O3 composition of 40/60 resulted in the highest viscosity value at all temperatures investigated, while the 60/40 composition recorded the lowest viscosity value. Besides, the increase in temperature of nanofluid shows a maximum viscosity reduction of 87.2 % as the temperature is increased from 0 to 100 °C. Also, the rheological analysis on a hybrid nanofluid for all compositions of Al2O3-Fe2O3 indicates a Newtonian fluid characteristic. The experimental research data was utilized to create an artificial neural network (ANN)-based architecture. An autoregressive method called the Bayesian approach was adopted for training hyperparameters. During model training, the autoregressive technique assisted in achieving outstanding correlation values of more than 99.99 % with minimal mean squared errors as low as 0.000036.

Investigating the Association Between Carbon Dioxide (CO2) Emissions and Energy Consumption on the Economic Growth of Clusterized Countries in the World

This research aims to investigate the relationship between carbon dioxide (CO2) emissions and energy consumption on the economic growth of countries in the world based on Gross Domestic Product (GDP). We use a large volume of data related to CO2 emissions per capita and energy consumption per capita of each country in the world, so a clustering process is needed using the K-Means method, which divides the country into three cluster labels: high, medium, and low. Then, we performed multiple linear regression using the Generalized Least Square with Autoregressive (GLSAR) method for data that did not satisfy the Best Linear Unbiased Estimator (BLUE) criteria. The results are that countries included in the high cluster have a closer relationship between CO2 emissions and energy consumption to GDP than the medium cluster. Likewise, for the medium cluster against the low cluster.

Time-Series Analysis of Oxygen as an Important Environmental Parameter for Monitoring Diversity Hotspot Ecosystems: An Example of a River Sinking into the Karst Underground

Predicting variations in dissolved oxygen concentration (DO) is important for management and environmental monitoring of aquatic ecosystems. Regression analyses and univariate and multivariate time-series analyses based on autoregressive methods were performed to investigate oxygen conditions in the Pivka River, Slovenia. The monitoring site was established upstream where the river sinks into the karst cave Postojnska jama, which hosts one of the richest subterranean faunas yet studied worldwide. It was found that abnormal variations of DO started to be noticeable at values of DO < 3 mg/L and became more pronounced until the ecosystem reached fully anoxic conditions. The abnormal fluctuations during the critical summer period were due to environmental conditions, organic load and resident biota. Predictions for future detection of anomalies in DO values were made from stable residuals of the measured data, and it was demonstrated that the model could be used to obtain a reliable estimate for a short period, such as one day. The example presented an analysis pipeline based on specific and established threshold DO values, and it is particularly important for ecosystems with diversity hotspots where prolonged low DO values can pose a threat to their biota.

Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials.

Plant breeding field trials are typically arranged as a row by column rectangular lattice. They have been widely analysed using linear mixed models in which low order autoregressive integrated moving average (ARIMA) time series models, and the subclass of separable lattice processes, are used to account for two-dimensional spatial dependence between the plot errors. A separable first order autoregressive model has been shown to be particularly useful in the analysis of plant breeding trials. Recently, tensor product penalised splines (TPS) have been proposed to model two-dimensional smooth variation in field trial data. This represents a non-stochastic smoothing approach which is in contrast to the autoregressive (AR) approach which models a stochastic covariance structure between the lattice of errors. This paper compares the AR and TPS methods empirically for a large set of early generation plant breeding trials. Here, the fitted models include information on genetic relatedness among the entries being evaluated. This provides a more relevant framework for comparison than the assumption of independent genetic effects. Judged by Akaike Information Criteria (AIC), the AR models were a better fit than the TPS model for more than 80% of trials. In the cases where the TPS model provided a better fit it did so by only a small amount whereas the AR models made a substantial improvement across a range of trials. When the AR and TPS models differ, there can be marked differences in the ranking of genotypes between the two methods of analysis based on their predicted genetic effects. Using the best fitting model for a trial as the benchmark, the rate of mis-classification of entries for selection was greater for the TPS model than the AR models. This has important practical implications for breeder selection decisions.

Factors Influencing Domestic Credit to Private Sector in Malaysia: Application of Vector Auto-Regressive Method

Factors Influencing Domestic Credit to Private Sector in Malaysia: Application of Vector Auto-Regressive Method

Fault Diagnosis of Diesel Engine Valve Clearance Based on Wavelet Packet Decomposition and Neural Networks

In order to improve the accuracy of engine valve clearance fault diagnosis, in this study, a fault identification algorithm based on wavelet packet decomposition and an artificial neural network is proposed. Firstly, the vibration signals of the engine cylinder head were collected, and different levels of noise were superimposed on the extended data sets. Then, the test data were decomposed into wavelet packets, and the power spectrum of the sub-band signal was analyzed using the autoregressive power spectrum density estimation method. A group of values were obtained from the power spectrum integration to form the fault eigenvalue. Finally, a neural network model was designed to classify the fault eigenvalues. In the training process, the test data set was divided into three parts, the training set, the verification set, and the test set, and the dropout layer was added to avoid the overfitting phenomenon of the neural network. The experimental results show that the wavelet packet neural network model in this paper has a good diagnostic accuracy for data with different levels of noise.

PENERAPAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) UNTUK MERAMALKAN PENERBANGAN DOMESTIK PADA TIGA BANDAR UDARA DI PULAU JAWA

The number of flights is a thing to measure the marketing performance of aviation services. Forecasting the number of flights is done so that airlines can make decisions in increasing the number of passengers and revenue. Forecasting the number of flights at various airports has relationship between time and location. The suitable method for forecasting the number of flights is Generalized Space Time Autoregressive (GSTAR) method. GSTAR is a method that used for forecasting time series data that has a relationship between time and location and has heterogeneous characteristics. This study applied the GSTAR method to model and forecast the number of domestic flights at three airports in Java, namely Husein Sastranegara Airport Bandung, Ahmad Yani Semarang, and Juanda Surabaya. The research chose those three airports because the impact of Covid-19 is very severe in that area. The weight used in this study is the distance inverse weight. The resulting model is a model with differencing 1, autoregressive order 1, and spatial order limited to 1 so that the model formed is the GSTAR model (11)-I(1). The GSTAR (11)-I(1) meets the assumptions of residual white noise and normal multivariate. The model also has sMAPE values for each airport: 2.60%, 4.18%, and 9.89%. Therefore, it can be concluded that the forecasting results of Husein Sastranegara Airport Bandung, Ahmad Yani Airport Semarang, and Surabaya Juanda Airport are very accurate.

Responsiveness of Headline Inflation to the Dynamics of Monetary Aggregates in Nigeria: A Structural Vector Autoregressive (SVAR) Approach

This paper contributes to the literature on the monetary transmission mechanism by using monthly data (January 2001 to December 2022) from the Central Bank of Nigeria (CBN) Statistical Bulletin to investigate how headline inflation responds to changes in monetary aggregates in Nigeria. The structural vector auto regression (SVAR) method by which the impulse response functions (IRF) and variance decomposition were used to examine the response of inflation to changes in narrow money (M1), M2, M3, and the interbank call rate In deviation from economic theory, the study found that positive shocks to M1 and M2 caused inflation to decline insignificantly. Positive shocks to M3 had an insignificant positive impact on inflation both in the short- and long run. This finding highlights that shocks to M3 are not a significant source of inflation pressure, and its control may not be a useful tool for the CBN to tame inflation in Nigeria. The study found that inflation declined insignificantly in response to positive shocks to the interbank call rate. This finding suggests that hiking interest rates have not been effective in addressing the problem of inflation in Nigeria. Overall, the results showed that using M1, M2, and M3 and interbank call rates is less potent in controlling inflation in Nigeria. Hence, this paper recommends reducing the number of financially excluded Nigerians by encouraging banking habits among Nigerians, especially those in un served and underserved rural areas.