Results 1 to 10 of about 2,317 (164)
Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter [PDF]
Sensors, 2015 To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed.
Lei Huang
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ARMA Model for Tracking Accelerated Corrosion Damage in a Steel Beam [PDF]
SensorsThis paper proposes an enhanced vibration-based damage detection index leveraging autoregressive moving average (ARMA) time-series modeling. The method relies on the fact that material deterioration alters the vibration features of the structure.
Sina Zolfagharysaravi +7 more
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Time-dependent ARMA modeling of genomic sequences [PDF]
BMC Bioinformatics, 2008 Background Over the past decade, many investigators have used sophisticated time series tools for the analysis of genomic sequences. Specifically, the correlation of the nucleotide chain has been studied by examining the properties of the power spectrum.
Schonfeld Dan +3 more
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IEEE AccessHyperparameters of Autoregressive Moving Average (ARMA) modeling are the number of AR coefficients and the number of MA coefficients. The hyperparameter selection (HS) in ARMA modeling plays a critical role and can dominate the coefficient (parameter ...
Soosan Beheshti, Vedant Bommanahally
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Comparing PAR and MPAR Models to Modeling the Monthly River Flow Rates Time Series Under the Influence of Meteorological Factors, The Case Study: Nazloochai River [PDF]
مجله مدل سازی در مهندسی, 2018 For over three decades, hydrologists were recommended multivariate models to describe and modeling complex hydrology data. While recently the multivariate models in hydrology is discussed.
Mohammad Nazeri Tahrudi
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Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting
Entropy, 2021 Modeling and analysis of time series are important in applications including economics, engineering, environmental science and social science. Selecting the best time series model with accurate parameters in forecasting is a challenging objective for ...
Zheng Fang +3 more
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Evaluation of Combined ARMA-ARCH and BL-ARCH models in Modeling Lake Urmia water level [PDF]
علوم و مهندسی آبیاری, 2017 Many nonlinear models have been developed based on the mean errors modeling. However, the non-linear models with Autoregressive conditional heteoscedasticity are based on variance modeling. These models are combined with linear models, partly to increase
Mohammad Nazeri Tahrudi +3 more
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Heliyon, 2023 The article focuses on analyzing the robustness of Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in unemployment rate estimation. In this context, a stochastic trend in the unemployment rate was determined
Dilek Surekci Yamacli, Serhan Yamacli
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Functional clustering of periodic transcriptional profiles through ARMA(p,q). [PDF]
PLoS ONE, 2010 BackgroundGene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time ...
Ning Li +5 more
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Mathematics, 2022 Predicting and modeling time-to-events data is a crucial and interesting research area. For modeling and predicting such types of data, numerous statistical models have been suggested and implemented. This study introduces a new statistical model, namely,
Zubair Ahmad +3 more
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