Bayesian estimation and model selection of a multivariate smooth transition autoregressive model
Sign Up to like & getrecommendations! Published in 2019 at "Environmetrics"
DOI: 10.1002/env.2615
Abstract: The multivariate smooth transition autoregressive model with order k (M‐STAR)(k) is a nonlinear multivariate time series model able to capture regime changes in the conditional mean. The main aim of this paper is to develop… read more here.
Keywords: autoregressive model; transition autoregressive; smooth transition; transition ... See more keywords
Fractional Volterra LMS algorithm with application to Hammerstein control autoregressive model identification
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3362-z
Abstract: In the present study, strength of fractional-order adaptive signal processing through fractional Volterra least mean square (FV-LMS) algorithm is exploited for Hammerstein nonlinear control autoregressive model (HN-CAR) identification. The FV-LMS method is a generalization of… read more here.
Keywords: autoregressive model; fractional volterra; control autoregressive; model ... See more keywords
Autoregressive model of an underwater acoustic channel in the frequency domain
Sign Up to like & getrecommendations! Published in 2022 at "Applied Acoustics"
DOI: 10.1016/j.apacoust.2021.108397
Abstract: Abstract The underwater acoustic (UWA) channel model based on the multipath delay–amplitude or the angle of departure (AoD)–angle of arrival (AoA) require many parameters to describe a broadband underwater acoustic (UWA) channel. In this paper,… read more here.
Keywords: autoregressive model; channel; model; channel frequency ... See more keywords
An innovative Bayesian system identification method using autoregressive model
Sign Up to like & getrecommendations! Published in 2019 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2019.106289
Abstract: Abstract This paper proposes an innovative Bayesian method for system identification based on autoregressive (AR) model. The dynamics of a structure is first modeled by an AR model. Due to measurement noise and modeling errors… read more here.
Keywords: autoregressive model; method; model; system identification ... See more keywords
Analysis of air quality parameters on climate change phenomenon using Markov autoregressive model
Sign Up to like & getrecommendations! Published in 2024 at "Cogent Engineering"
DOI: 10.1080/23311916.2024.2421284
Abstract: Abstract In the present era, increased levels of air pollution have become a major threat to humankind, ecosystems, and climate. Nowadays, the level of noxious emissions in the environment is increasing tremendously owing to industrialization,… read more here.
Keywords: autoregressive model; using markov; air; air quality ... See more keywords
Blind modal identification using generalized multivariate autoregressive model and extended joint eigenvalue decomposition
Sign Up to like & getrecommendations! Published in 2025 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/adccee
Abstract: This study introduces the concept of blind source separation (BSS) based on a multivariate autoregressive (AR) model in the field of operational modal analysis (OMA), with improvements and extensions. A novel blind modal identification method… read more here.
Keywords: autoregressive model; multivariate autoregressive; method; model ... See more keywords
Hypertension Monitoring by a Real Time Management System for Patients in Community and Its Data Mining by Vector Autoregressive Model
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3240084
Abstract: Blood pressure has a 24-hour repetitive and regular variation which shows circadian rhythm. Using the multivariate time series analysis method of vector autoregressive model, we could realize the simultaneous prediction for both systolic and diastolic… read more here.
Keywords: blood; vector autoregressive; blood pressure; autoregressive model ... See more keywords
A Time-Varying Autoregressive Model for Characterizing Nonstationary Processes
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2018.2880086
Abstract: This letter presents a time-varying autoregressive (TVAR) model aiming to characterize nonstationary behaviors often observed in real-world processes, which cannot be properly described by autoregressive processes such as first-order Markov and random-walk models. Specifically, general… read more here.
Keywords: varying autoregressive; autoregressive model; tvar model; model ... See more keywords
Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Healthcare Engineering"
DOI: 10.1155/2022/4802743
Abstract: The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout the globe suffered from this pandemic, forcing various governments to apply different restrictions to reduce its impact.… read more here.
Keywords: neural network; model nnar; network autoregressive; covid ... See more keywords
A Unified Test for the AR Error Structure of an Autoregressive Model
Sign Up to like & getrecommendations! Published in 2022 at "Axioms"
DOI: 10.3390/axioms11120690
Abstract: A direct application of autoregressive (AR) models with independent and identically distributed (iid) errors is sometimes inadequate to fit the time series data well. A natural alternative is further to assume the model errors following… read more here.
Keywords: test; unified test; model; error structure ... See more keywords
A Mixture Autoregressive Model Based on an Asymmetric Exponential Power Distribution
Sign Up to like & getrecommendations! Published in 2023 at "Axioms"
DOI: 10.3390/axioms12020196
Abstract: In nonlinear time series analysis, the mixture autoregressive model (MAR) is an effective statistical tool to capture the multimodality of data. However, the traditional methods usually need to assume that the error follows a specific… read more here.
Keywords: exponential power; asymmetric exponential; model; distribution ... See more keywords