Autoregressive Moving Average (ARMA) Models of Neuromuscular System

1982 ◽  
Vol 15 (4) ◽  
pp. 1205-1210
Author(s):  
G.C. Agarwal ◽  
S.M. Goodarzi ◽  
W.D. O'Neill ◽  
G.L. Cottlieb
Keyword(s):  
Moving Average ◽  
Autoregressive Moving Average ◽  
Neuromuscular System ◽  
Arma Models

Simulation of Homogeneous Two-Dimensional Random Fields: Part II—MA and ARMA Models

1992 ◽  
Vol 59 (2S) ◽  
pp. S270-S277 ◽  
Author(s):  
Pol D. Spanos ◽  
Marc P. Mignolet
Keyword(s):  
Random Fields ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Mathematical Form ◽  
Simulation Algorithm ◽  
Arma Models ◽  
Spectral Matrix ◽  
Target Power ◽  
Ar Models

Alternatively to the autoregressive (AR) models examined in Part I, the determination of moving average (MA) algorithms for simulating realizations of twodimensional random fields with a specified (target) power spectrum is presented. First, the mathematical form of these models is addressed by considering infinitevariate vector processes of an appropriate spectral matrix. Next, the MA parameters are determined by relying on the maximization of an energy-like quantity. Then, a technique is formulated to derive an autoregressive moving average (ARMA) simulation algorithm from a prior MA approximation by relying on the minimization of frequency domain errors. Finally, these procedures are critically assessed and an example of application is presented.

scholarly journals A Fuzzy Set-Valued Autoregressive Moving Average Model and Its Applications

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 324 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Liang Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Complex Data ◽  
Arma Models ◽  
Linguistic Data ◽  
Series Analysis

Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challenging problems in time series analysis and mathematical statistics. In this study, we extended the ARMA model to the case of linguistic data that can be modeled by some symmetric fuzzy sets, and where the relations between the linguistic data of the time series can be considered as the ordinary stochastic correlation rather than fuzzy logical relations. Therefore, the concepts of set-valued or interval-valued random variables can be employed, and the notions of Aumann expectation, Fréchet variance, and covariance, as well as standardized process, were used to construct the ARMA model. We firstly determined that the estimators from the least square estimation of the ARMA (1,1) model under some L2 distance between two sets are weakly consistent. Moreover, the justified linguistic data-valued ARMA model was applied to forecast the linguistic monthly Hang Seng Index (HSI) as an empirical analysis. The obtained results from the empirical analysis indicate that the accuracy of the prediction produced from the proposed model is better than that produced from the classical one-order, two-order, three-order autoregressive (AR(1), AR(2), AR(3)) models, as well as the (1,1)-order autoregressive moving average (ARMA(1,1)) model.

Simulation of Homogeneous Two-Dimensional Random Fields: Part I—AR and ARMA Models

1992 ◽  
Vol 59 (2S) ◽  
pp. S260-S269 ◽  
Author(s):  
Marc P. Mignolet ◽  
Pol D. Spanos
Keyword(s):  
Power Spectrum ◽  
Random Fields ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Simulation Algorithm ◽  
Two Dimensional ◽  
Arma Models ◽  
Spectral Matrix ◽  
Target Power

The determination of autoregressive (AR) and autoregressive moving average (ARMA) algorithms for simulating realizations of two-dimensional random fields with a specified (target) power spectrum is examined. The form of both of these models is justified first by considering infinite-variate vector processes of appropriate spectral matrix. Next, the AR parameters are selected to achieve the minimum of a positive integral. Then, a technique is formulated to derive an ARM A simulation algorithm from the prior AR approximation by relying on the minimization of frequency domain errors. Finally, these procedures are critically assessed and an example of application is presented.

scholarly journals Predicting IGS RTS Corrections Using ARMA Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mingyu Kim ◽  
Jeongrae Kim
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Optimal Order ◽  
Autoregressive Moving Average ◽  
Arma Models ◽  
Time Service ◽  
Prediction Time ◽  
Clock Correction ◽  
Rms Error ◽  
The Difference

An autoregressive moving average neural network (ARMANN) model is applied to predict IGS real time service corrections. ARMA coefficients are determined by applying a neural network to IGS02 orbit/clock corrections. Other than the ARMANN, the polynomial and ARMA models are tested for comparison. An optimal order of each model is determined by fitting the model to the correction data. The data fitting period for training the models is 60 min. and the prediction period is 30 min. The polynomial model is good for the fitting but bad for the prediction. The ARMA and ARMANN have a similar level of accuracies, but the RMS error of the ARMANN is smaller than that of the ARMA. The RMS error of the ARMANN is 0.046 m for the 3D orbit correction and 0.070 m for the clock correction. The difference between the ARMA and ARMANN models becomes significant as the prediction time is increased.

scholarly journals Bootstrap Order Determination for ARMA Models: A Comparison between Different Model Selection Criteria

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Livio Fenga
Keyword(s):  
Selection Criteria ◽  
Moving Average ◽  
Information Criterion ◽  
Autoregressive Moving Average ◽  
Order Selection ◽  
Arma Models ◽  
Final Prediction Error ◽  
Novel Method ◽  
Order Determination ◽  
Selection Of

The present paper deals with the order selection of models of the class for autoregressive moving average. A novel method—previously designed to enhance the selection capabilities of the Akaike Information Criterion and successfully tested—is now extended to the other three popular selectors commonly used by both theoretical statisticians and practitioners. They are the final prediction error, the Bayesian information criterion, and the Hannan-Quinn information criterion which are employed in conjunction with a semiparametric bootstrap scheme of the type sieve.

Time Series Modeling of Earthquake Ground Motions Using ARMA-GARCH Models

2013 ◽  
Vol 470 ◽  
pp. 240-243 ◽  
Author(s):  
Jeng Hsiang Lin
Keyword(s):  
Ground Motion ◽  
Moving Average ◽  
Ground Motions ◽  
Structural Response ◽  
Earthquake Ground Motion ◽  
Response Analysis ◽  
Practical Implementation ◽  
Autoregressive Moving Average ◽  
Arma Models ◽  
Earthquake Models

Engineers are well aware that, due to the stochastic nature of earthquake ground motion, the information obtained from structural response analysis using scant records is quite unreliable. Thus, providing earthquake models for specific sites or areas of research and practical implementation is essential. This paper presents a procedure for the modeling strong earthquake ground motion based on autoregressive moving average (ARMA) models. The Generalized autoregressive conditional heteroskedasticity (GARCH) model is used to simulate the time-varying characteristics of earthquakes.

scholarly journals Pendugaan Imbal Hasil Saham dengan Model Autoregressive Moving Average

2021 ◽  
Vol 3 (2) ◽  
pp. 140-154
Author(s):  
Grifin Ryandi Egeten ◽  
Berlian Setiawaty ◽  
Retno Budiarti
Keyword(s):  
Maximum Likelihood ◽  
Central Asia ◽  
Stock Returns ◽  
Stock Return ◽  
Moving Average ◽  
Arma Model ◽  
Ar Model ◽  
Autoregressive Moving Average ◽  
Arma Models ◽  
Unknown Parameters

ABSTRAKSeorang investor pada umumnya berharap untuk membeli suatu saham dengan harga yang rendah dan menjual saham tersebut dengan harga yang lebih tinggi untuk memperoleh imbal hasil yang tinggi. Namun, kapan waktu yang tepat melakukannya menjadi tantangan tersendiri bagi para investor. Oleh sebab itu, dibutuhkan suatu model yang mampu menduga imbal hasil saham dengan baik, salah satunya adalah model autoregressive moving average (ARMA). Tujuan dari penelitian ini adalah untuk menerapkan model autoregressive (AR), model moving average (MA), atau model autoregressive moving average (ARMA) pada data observasi untuk menduga imbal hasil saham bank central asia (BCA). Terdapat empat prosedur dalam membangun sebuah model AR, MA atau ARMA. Pertama, data yang digunakan harus weakly stationary. Kedua, orde dari model harus diidentifikasi untuk memperoleh model yang terbaik. Ketiga, parameter setiap model harus ditentukan. Keempat, kelayakan model harus diperiksa dengan melakukan analisis residual untuk memperoleh model yang terbaik. Pada akhirnya, model ARMA (1,1) adalah model terbaik dan akurat dalam menduga imbal hasil saham BCA. ABSTRACTGenerally, investor always wish to be able to buy a stock at a low price and sell it at a higher price to obtain high returns. However, when is the best time to buy or sell it is a challenge for investor. Therefore, proper models are needed to predict a stock return, one of them is autoregressive moving average (ARMA) model. The first purpose of this paper is to apply the autoregressive (AR), moving average (MA) or ARMA models to the observations to predict stock returns. There are four procedures which is used to build an AR, MA, or ARMA model. First, the observations must be weakly stationary. Second, the order of the models must be identified to obtain the best model. Third, the unknown parameters of the models are estimated by maximum likelihood. Fourth, through residual analysis, diagnostic checks are performed to determine the adequacy of the model. In this paper, stock returns of BCA are used as data observation. Finally, the ARMA (1,1) model is the best model and appropriate to predict the stock returns BCA in the future.

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