Output Only Functional Series Time Dependent AutoRegressive Moving Average (FS-TARMA) Modelling of Tool Acceleration Signals for Wear Estimation

2015 ◽  
pp. 111-122 ◽  
Author(s):  
B. H. Aghdam ◽  
E. Cigeroglu ◽  
M. H. Sadeghi
Keyword(s):  
Moving Average ◽  
Time Dependent ◽  
Autoregressive Moving Average ◽  
Functional Series ◽  
Output Only ◽  
Acceleration Signals

scholarly journals Non - stationary time - dependent ARMA random vibration modeling, analysis, and SHM with wind turbine applications

2016 ◽  
Author(s):  
Λουίς Δαβίδ Αβεντάνο Βαλένσια
Keyword(s):  
Wind Turbine ◽  
Random Vibration ◽  
Moving Average ◽  
Time Dependent ◽  
Autoregressive Moving Average ◽  
Stationary Time ◽  
Modeling Analysis ◽  
Vibration Modeling

Το κύριο θέμα αυτής της εργασίας είναι η ανάπτυξη αλγορίθμων για την μοντελοποίηση σημάτων εξόδου διέγερσης, χρόνου συχνότητας και την παρακολούθηση της υγείας των κατασκευών με μη στάσιμη απόκριση διέγερσης, βασιζόμενοι σε μη στάσιμα AutoRegressive Moving Average (TARMA) και Γραμμικά Μεταβαλλόμενα ARMA (LPV-ARMA) μοντέλα. Η εργασία που παρουσιάζεται σε αυτή την Διδακτορική Διατριβή χωρίζεται σε τρεις κύριες ενότητες (1) Την αποτελεσματική αναπαράσταση μη στάσιμων σημάτων εξόδου κατασκευών με χρονικά μεταβαλλόμενα δυναμικά χαρακτηριστικά και υπό σημαντική επιρροή αβεβαιότητας (λειτουργιάς της κατασκευής και περιβαλλοντικές συνθήκες) μέσω TARMA και LPV-ARMA μοντέλα (2) Την ανάπτυξη μεθόδων διάγνωσης και αναγνώρισης βλαβών που είναι ικανές να αντιμετωπίσουν τη μεταβλητότητα που προκαλείται στα δυναμικά χαρακτηριστικά της απόκρισης διέγερσης της κατασκευής λόγω αβεβαιότητας (3) Η αναπτυξη μαθηματικών εκφράσεων που μπορούν να χρησιμεύσουν για την λήψη ακριβής περιγραφών χρόνου και συχνότητας της συμπεριφοράς μη στάσιμων διεργασιών και μπορούν να χρησιμοποιηθούν για την ανάλυση της μη-στάσιμης συμπεριφοράς των δυναμικών χαρακτηριστικών κατασκευών με χρονικά-εξαρτώμενα δυναμικά χαρακτηριστικά.

On the Estimation of Nonstationary Functional Series TARMA Models: An Isomorphic Matrix Algebra Based Method1

2001 ◽  
Vol 123 (4) ◽  
pp. 601-610 ◽  
Author(s):  
George N. Fouskitakis ◽  
Spilios D. Fassois
Keyword(s):  
Matrix Algebra ◽  
Moving Average ◽  
Estimation Method ◽  
Algebraic Approach ◽  
Autoregressive Moving Average ◽  
Excellent Performance ◽  
Functional Series ◽  
Monte Carlo Experiments ◽  
Polynomial Operators ◽  
Improved Accuracy

Functional Series Time-dependent AutoRegressive Moving Average (TARMA) models form an important class of nonstationary stochastic models offering high parsimony, tracking of “fast” and “slow” variations, high accuracy and resolution, as well as accurate capturing of both resonances and antiresonances. This paper considers the estimation of Functional Series TARMA models with polynomial functional spaces based upon a novel matrix algebra that is isomorphic to that of the noncommutative ring of time-varying polynomial operators expressed in terms of the model’s functional spaces. The Generalized estimation method introduced offers important advantages, such as the use of properly contracted functional spaces that is necessary for the elimination of asymptotic bias errors, the ability to handle AR and MA functional spaces of different dimensionalities, as well as improved accuracy through a streamlined realization. The method’s excellent performance characteristics are confirmed via Monte Carlo experiments and comparisons with an earlier Polynomial-Algebraic approach and the adaptive Recursive Maximum Likelihood ARMA method.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

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