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Journal articles on the topic 'ARMA-modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Yao, Jing-Wu, and Sueo Sugimoto. "ARMA Modeling and Order Determination." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1996 (May 5, 1996): 113–19. http://dx.doi.org/10.5687/sss.1996.113.

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2

Chakraborty, M., and S. Prasad. "Multivariate ARMA modeling by scalar algorithms." IEEE Transactions on Signal Processing 41, no. 4 (1993): 1692–97. http://dx.doi.org/10.1109/78.212746.

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3

Moses, R., J. Cadzow, and A. Beex. "A recursive procedure for ARMA modeling." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 5 (1985): 1188–96. http://dx.doi.org/10.1109/tassp.1985.1164683.

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4

Jackson, L. B. "Noncausal ARMA modeling of voiced speech." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 10 (1989): 1606–8. http://dx.doi.org/10.1109/29.35403.

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5

Mignolet, Marc P., and Pol D. Spanos. "MA to ARMA modeling of wind." Journal of Wind Engineering and Industrial Aerodynamics 36 (January 1990): 429–38. http://dx.doi.org/10.1016/0167-6105(90)90326-8.

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6

Conte, J. P., K. S. Pister, and S. A. Mahin. "Nonstationary ARMA modeling of seismic motions." Soil Dynamics and Earthquake Engineering 11, no. 7 (1992): 411–26. http://dx.doi.org/10.1016/0267-7261(92)90005-x.

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7

Wei, Wei, and Hao Ma. "Wavelet-Based ARMA Model Application in Power Network." Applied Mechanics and Materials 121-126 (October 2011): 1509–13. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.1509.

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The theoretical properties of the ARMA model and the modeling process, then, the Shanghai power network and Shenzhen power network in China were established ARMA model and wavelet-based ARMA model fitting, prediction, and finally, to fit forecast The results were compared. It can be seen, combined with relatively good forecasting effect after wavelet.

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8

Menasri, A., M. Brahimi, R. Frank, and A. Bali. "ARMA Modeling of Artificial Accelerograms for Algeria." Applied Mechanics and Materials 105-107 (September 2011): 348–55. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.348.

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The main aim of this study is to examine on the real and simulated earthquakes effects. This paper deals with the use of ARMA models in earthquake engineering. The time-varying auto regressive moving average (ARMA) process is used as a simple yet efficient method for simulating earthquake ground motions. This model is capable of reproducing the nonstationary amplitude as well as the frequency content of the earthquake ground accelerations. The moving time-window technique is applied to synthesize the near field earthquakes, Chlef-1, Chlef-2, Chlef-3 and Attaf 1980 recorded on dense soils in Al

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9

Shouman, Shouman. "MODELING OF EGG SIGNALS USING ARMA COEFFICIENTS." International Conference on Electrical Engineering 2, no. 2 (1999): 245–52. http://dx.doi.org/10.21608/iceeng.1999.62507.

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10

Ismaeel, Hasnaa A., and Thafer R. Al-Badrany. "Modeling Some Weather Phenomena by Vector ARMA." IRAQI JOURNAL OF STATISTICAL SCIENCES 15, no. 27 (2018): 59–82. http://dx.doi.org/10.33899/iqjoss.2018.159248.

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11

Georgiou, Tryphon T., and Anders Lindquist. "A Convex Optimization Approach to ARMA Modeling." IEEE Transactions on Automatic Control 53, no. 5 (2008): 1108–19. http://dx.doi.org/10.1109/tac.2008.923684.

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12

SAKAI, Hideaki. "ARMA Modeling by the Circular Lattice Method." Transactions of the Institute of Systems, Control and Information Engineers 2, no. 3 (1989): 88–96. http://dx.doi.org/10.5687/iscie.2.88.

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13

Novotny, Vladimir, and Shuhai Zheng. "Rainfall‐Runoff Transfer Function by ARMA Modeling." Journal of Hydraulic Engineering 115, no. 10 (1989): 1386–400. http://dx.doi.org/10.1061/(asce)0733-9429(1989)115:10(1386).

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14

Miyanaga, Yoshikazu, Noriyuki Ohtsuki, and Koji Tochinai. "Adaptive ARMA lattice modeling for speech processing." Journal of the Acoustical Society of Japan (E) 11, no. 3 (1990): 173–82. http://dx.doi.org/10.1250/ast.11.173.

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15

Nichols, S. T., M. R. Smith, and M. J. E. Salami. "Application of ARMA Modeling to Multicomponent Signals." IFAC Proceedings Volumes 18, no. 5 (1985): 1473–78. http://dx.doi.org/10.1016/s1474-6670(17)60773-0.

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16

Ortigueira, Manuel Duarte. "A three step algorithm for ARMA modeling." Signal Processing 15, no. 1 (1988): 23–30. http://dx.doi.org/10.1016/0165-1684(88)90025-4.

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17

Lobo, Arthur P., and William A. Ainsworth. "Evaluation of a glottal ARMA modeling scheme." Journal of the Acoustical Society of America 86, S1 (1989): S76. http://dx.doi.org/10.1121/1.2027641.

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18

Hana Rahma Trifanni, Dony Permana, Nonong Amalita, and Atus Amadi Putra. "Time Series Modeling on Stock Return at PT. Telecommunication Indonesia Tbk." UNP Journal of Statistics and Data Science 1, no. 1 (2023): 1–7. http://dx.doi.org/10.24036/ujsds/vol1-iss1/8.

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One of the time series data modeling is the ARMA model which assumes constant volatility. However, in economic and financial data, there are many cases where volatility is not constant. This results in the occurrence of heteroscedasticity problems in the residuals, so a GARCH model is needed. In addition to heteroscedasticity, another problem with residuals is the asymmetric effect or leverage effect. For that we need asymmetric GARCH modeling. This study aims to compare the accuracy of the ARMA, GARCH, and asymmetric GARCH models. This research is an applied research. The data used is daily s

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19

Akpan, Emmanuel Alphonsus, Imoh Udo Moffat, and Ntiedo Bassey Ekpo. "Arma-Arch Modeling Of The Returns Of First Bank Of Nigeria." European Scientific Journal, ESJ 12, no. 18 (2016): 257. http://dx.doi.org/10.19044/esj.2016.v12n18p257.

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This study looks at a possible combination of both the ARMA and ARCH-types models to form a single model such as ARMA-ARCH that will completely model the linear and non-linear features of financial data. The data used for this study are daily closing share prices of First Bank of Nigeria plc from January 4, 2000 to December 31, 2013 and were obtained from the Nigerian Stock Exchange. The share price series was found to be nonstationary while the returns series which is the first difference of log of the share price series was found to be stationary. This study provides evidence to show that AR

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20

Sulistiowati, Dwi, Maya Sari Syahrul, and Iswan Rina. "Pemodelan Harga Saham Menggunakan Arma-Garch." Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta 1, no. 2 (2022): 89–93. http://dx.doi.org/10.47233/jppie.v1i2.532.

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Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were used for modeling with heteroscedasticity data. This study aims to determine the time series model on the stock price data of PT Triputra Agro Persada Tbk. (TAPG) with modeling ARMA, ARCH and GARCH. Based on the smallest Akaike Information Criterion (AIC) and Schwarz Criterion (SC), it shows that the ARMA(1,0)-GARCH(2,1) model is the best model for predicting the value of TAPG stock prices.

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21

Fang, Zheng, David L. Dowe, Shelton Peiris, and Dedi Rosadi. "Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting." Entropy 23, no. 12 (2021): 1601. http://dx.doi.org/10.3390/e23121601.

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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 scientists and academic researchers. Hybrid models combining neural networks and traditional Autoregressive Moving Average (ARMA) models are being used to improve the accuracy of modeling and forecasting time series. Most of the existing time series models are selected by information-theoretic approaches, such as AIC, BIC, and HQ. This paper revis

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22

Cammarota, Camillo. "Inter-event Times Statistic in Stationary Processes: Nonlinear ARMA Modeling of Wind Speed Time Series." Nonlinear Phenomena in Complex Systems 24, no. 4 (2021): 370–81. http://dx.doi.org/10.33581/1561-4085-2021-24-4-370-381.

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The random sequence of inter-event times of a level-crossing is a statistical tool that can be used to investigate time series from complex phenomena. Typical features of observed series as the skewed distribution and long range correlations are modeled using non linear transformations applied to Gaussian ARMA processes. We investigate the distribution of the inter-event times of the level-crossing events in ARMA processes in function of the probability corresponding to the level. For Gaussian ARMA processes we establish a representation of this indicator, prove its symmetry and that it is inv

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23

Therrien, C. W., and C. H. Velasco. "An iterative Prony method for ARMA signal modeling." IEEE Transactions on Signal Processing 43, no. 1 (1995): 358–61. http://dx.doi.org/10.1109/78.365329.

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24

Laner, Markus, Philipp Svoboda, and Markus Rupp. "Parsimonious Network Traffic Modeling By Transformed ARMA Models." IEEE Access 2 (2014): 40–55. http://dx.doi.org/10.1109/access.2013.2297736.

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25

Bierens, Herman J. "ARMA Memory Index Modeling of Economic Time Series." Econometric Theory 4, no. 1 (1988): 35–59. http://dx.doi.org/10.1017/s0266466600011816.

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In this paper, it will be shown that if we condition a k-variate rational-valued time series process on its entire past, it is possible to capture all relevant information on the past of the process by a single random variable. This scalar random variable can be formed as an autoregressive moving average of past observations; Since economic data are usually reported in a finite number of digits, this result applies to virtually all economic time series. Therefore, economic time series regressions generally take the form of a nonlinear function of an autoregressive moving average of past observ

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26

Pramod, B. R., and S. C. Bose. "System Identification Using ARMA Modeling and Neural Networks." Journal of Engineering for Industry 115, no. 4 (1993): 487–91. http://dx.doi.org/10.1115/1.2901794.

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Stochastic system identification is an important tool for control of discrete dynamic systems. Among the modeling strategies developed for this purpose, Auto Regressive Moving Average (ARMA for discrete systems) models offer an accurate identification technique. The disadvantage with these models are that they are extremely complicated to implement on-line, especially for nonlinear time-variant systems. This paper utilizes a Neural Network structure for identification of stochastic processes and tracks system dynamics by on-line adjustments of network parameters. Neural dynamics is based on im

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27

Sadia, Farhana, Sarah Boyd, and Jonathan M. Keith. "Bayesian change-point modeling with segmented ARMA model." PLOS ONE 13, no. 12 (2018): e0208927. http://dx.doi.org/10.1371/journal.pone.0208927.

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28

de Waele, S., and P. M. Broersen. "Reliable LDA-spectra by resampling and ARMA-modeling." IEEE Transactions on Instrumentation and Measurement 48, no. 6 (1999): 1117–21. http://dx.doi.org/10.1109/19.816124.

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29

Krishnapura, Venugopal G., and Arthur Jutan. "Arma neuron networks for modeling nonlinear dynamical systems." Canadian Journal of Chemical Engineering 75, no. 3 (1997): 574–82. http://dx.doi.org/10.1002/cjce.5450750311.

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30

Moutari, Natatou Dodo, Abba Mallam Hassane, Barro Diakarya, and Saley Bisso. "The ARMA-APARCH-EVT Models Based on HAC in Dependence Modeling and Risk Assessment of a Financial Portfolio." European Journal of Pure and Applied Mathematics 14, no. 4 (2021): 1467–89. http://dx.doi.org/10.29020/nybg.ejpam.v14i4.4114.

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Multivariate modeling of dependence and its impact on risk assessment remains a major concern for financial institutions. Thus, the copula model, in particular Archimedean hierarchical copulas (HAC) appears as a promising alternative, capable to precisely capture the structure of dependence between financial variables. This study aims to widen the sphere of practical applicability of the HAC model combined with the ARMA-APARCH volatility forecast model and the extreme values theory (EVT). A sequential process of modeling of the VaR of a portfolio based on the ARMA-APARCH-EVT-HAC model is discu

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31

Bildirici, Melike, and Özgür Ersin. "Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns." Scientific World Journal 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/497941.

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The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN

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32

Chakraborty, M., and S. Prasad. "Multichannel ARMA modeling by least squares circular lattice filtering." IEEE Transactions on Signal Processing 42, no. 9 (1994): 2304–18. http://dx.doi.org/10.1109/78.317853.

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33

Petropulu, A. P. "Noncausal nonminimum phase ARMA modeling of non-Gaussian processes." IEEE Transactions on Signal Processing 43, no. 8 (1995): 1946–54. http://dx.doi.org/10.1109/78.403353.

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34

Yao, Jingwu, and Sueo Sugimoto. "ARMA Modeling via Bounded-Real Functions and Lattice Filters." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1998 (May 5, 1998): 93–98. http://dx.doi.org/10.5687/sss.1998.93.

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35

Pappas, S. Sp, L. Ekonomou, P. Karampelas, S. K. Katsikas, and P. Liatsis. "Modeling of the grounding resistance variation using ARMA models." Simulation Modelling Practice and Theory 16, no. 5 (2008): 560–70. http://dx.doi.org/10.1016/j.simpat.2008.02.009.

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36

Xin, Cao, Sheng Zhaohan, and Xu Nanrong. "Modeling for a Class of Nonstationary ARMA Series 1." IFAC Proceedings Volumes 23, no. 8 (1990): 101–3. http://dx.doi.org/10.1016/s1474-6670(17)51991-6.

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37

Nikias, Chrysostomos L., and Renlong Pan. "ARMA modeling of fourth-order cumulants and phase estimation." Circuits, Systems, and Signal Processing 7, no. 3 (1988): 291–325. http://dx.doi.org/10.1007/bf01599973.

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38

Kuroda, Y., H. Uchida, and Y. Ohmasa. "Multivariate arma modeling with spline functions for reactor noise." Progress in Nuclear Energy 15 (January 1985): 849–52. http://dx.doi.org/10.1016/0149-1970(85)90118-0.

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39

Zhou, Yang, Bilal Muhammad Khan, Jin Yong Choi, and Yoram Cohen. "Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities." Water 13, no. 16 (2021): 2312. http://dx.doi.org/10.3390/w13162312.

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Water use patterns were explored for three small communities that are located in proximity to agricultural fields and rely on their local wells for potable water supply. High-resolution water use data, collected over a four-year period, revealed significant temporal variability. Monthly, daily, and hourly water use patterns were well described by autoregressive moving average (ARMA) models. Model development was supported by unsupervised clustering analysis via self-organizing maps (SOMs) that revealed similarities of water use patterns and confirmed the time-series water use model attributes.

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40

Qu, Youyang. "Prediction of aircraft engine parameters based on ARMA model." Journal of Physics: Conference Series 2252, no. 1 (2022): 012065. http://dx.doi.org/10.1088/1742-6596/2252/1/012065.

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Abstract The high-pressure rotor speeds, low-pressure rotor speeds, and exhaust temperature of the aircraft engine are the key parameters reflecting the performance of aircraft engines. To realize the trend monitoring during the flight test and the processing of data outliers in flight data recorder, the time series analysis and modeling method is used to establish a suitable ARMA model through data processing, series property analysis, model identification, order determination, modeling, model diagnosis and other steps. Fit the real flight test data of an engine. The results show that the pre

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41

Qu, Youyang. "Prediction of aircraft engine parameters based on ARMA model." Journal of Physics: Conference Series 2252, no. 1 (2022): 012065. http://dx.doi.org/10.1088/1742-6596/2252/1/012065.

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Abstract The high-pressure rotor speeds, low-pressure rotor speeds, and exhaust temperature of the aircraft engine are the key parameters reflecting the performance of aircraft engines. To realize the trend monitoring during the flight test and the processing of data outliers in flight data recorder, the time series analysis and modeling method is used to establish a suitable ARMA model through data processing, series property analysis, model identification, order determination, modeling, model diagnosis and other steps. Fit the real flight test data of an engine. The results show that the pre

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42

AMIAN, MEHDI, and S. KAMALEDIN SETAREHDAN. "MOTION ARTIFACT REDUCTION IN FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS BY AUTOREGRESSIVE MOVING AVERAGE MODELING BASED KALMAN FILTERING." Journal of Innovative Optical Health Sciences 06, no. 04 (2013): 1350035. http://dx.doi.org/10.1142/s1793545813500351.

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Functional near infrared spectroscopy (fNIRS) is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2) and deoxyhemoglobin (HHb) concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in s

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43

Haneda, Yoichi, Shoji Makino, Yutaka Kaneda, and Nobuo Koizumi. "ARMA modeling of a room transfer function at low frequencies." Journal of the Acoustical Society of Japan (E) 15, no. 5 (1994): 353–55. http://dx.doi.org/10.1250/ast.15.353.

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44

Nowicka-Zagrajek, J., and R. Weron. "Modeling electricity loads in California: ARMA models with hyperbolic noise." Signal Processing 82, no. 12 (2002): 1903–15. http://dx.doi.org/10.1016/s0165-1684(02)00318-3.

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45

Tomczak, Marc, and El-Hadi Djermoune. "A subband ARMA modeling approach to high-resolution NMR spectroscopy." Journal of Magnetic Resonance 158, no. 1-2 (2002): 86–98. http://dx.doi.org/10.1016/s1090-7807(02)00064-2.

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46

Zheng, Yuanjin, Zhiping Lin, and David B. H. Tay. "State-dependent vector hybrid linear and nonlinear ARMA modeling: Theory." Circuits, Systems, and Signal Processing 20, no. 5 (2001): 551–74. http://dx.doi.org/10.1007/bf01201978.

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47

Zheng, Yuanjin, Zhiping Lin, and David B. H. Tay. "State-dependent vector hybrid linear and nonlinear ARMA modeling: Applications." Circuits, Systems, and Signal Processing 20, no. 5 (2001): 575–97. http://dx.doi.org/10.1007/bf01201979.

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48

Wang, Jiangli, Yu Chen, and Weiping Zhang. "Parsimonious Mean-Covariance Modeling for Longitudinal Data with ARMA Errors." Journal of Systems Science and Complexity 32, no. 6 (2019): 1675–92. http://dx.doi.org/10.1007/s11424-019-7354-6.

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49

Pappas, S. Sp, L. Ekonomou, D. Ch Karamousantas, G. E. Chatzarakis, S. K. Katsikas, and P. Liatsis. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models." Energy 33, no. 9 (2008): 1353–60. http://dx.doi.org/10.1016/j.energy.2008.05.008.

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50

Khazaeiathar, Mahshid, Reza Hadizadeh, Nasrin Fathollahzadeh Attar, and Britta Schmalz. "Daily Streamflow Time Series Modeling by Using a Periodic Autoregressive Model (ARMA) Based on Fuzzy Clustering." Water 14, no. 23 (2022): 3932. http://dx.doi.org/10.3390/w14233932.

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The behavior of hydrological processes is periodic and stochastic due to the influence of climatic factors. Therefore, it is crucial to develop the models based on their periodicity and stochastic nature for prediction. Furthermore, forecasting the streamflow, as one of the main components of the hydrological cycle, is a primary subject. In this study, a statistical method, Fuzzy C-means clustering, was used to find the periodicity in the daily discharge time series, whereas autoregressive moving average, ARMA, was used in modeling every cluster. Dividing the daily stream flow time series into

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