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Journal articles on the topic 'Dynamic series'

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Published: 7 June 2025
Last updated: 16 July 2025

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1

Roslan, Muhammad Amirul Asyraf, Nadia Nazieha Nanda, and Siti Hajar Yusoff. "SERIES-SERIES AND SERIES-PARALLEL COMPENSATION TOPOLOGIES FOR DYNAMIC WIRELESS CHARGING." IIUM Engineering Journal 22, no. 2 (2021): 199–209. http://dx.doi.org/10.31436/iiumej.v22i2.1660.

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Electric vehicles (EV) have gained worldwide attention since the implementation of a wireless power transfer (WPT) to charge their batteries. With WPT, it can be very convenient for EV to be charged dynamically. Nevertheless, there are some issues in dynamic WPT, such as maintaining the power transfer efficiency. Several factors that lead to these problems include disruption of the alignment and the optimum distance between the transmitter and receiver coils. It is thus contributing to the loss of power efficiency when charging the EV. Not to mention, manufacturers build different specifications of EV charging station for different types of EV models in order to meet customer demands. An incompatible charging device will not utilize EV wireless charging to its maximum potential. Hence, to improve the power output capability as well as stabilizing the maximum power transfer during the charging process, a compensation circuit is added to the system. This article focuses on comparing two available compensation circuits (series-series (SS) topology and series-parallel (SP) topology) under the application of dynamic wireless charging. The simulations are conducted using NI Multisim based on the relationship of power transfer efficiency with resonance frequency, coefficient of coupling, and the load resistance. The WPT efficiency for SP-topology shows that it is sensitive to the change of resonance frequency and coupling coefficient, whereas SS-topology maintains good efficiency during the WPT process. Nonetheless, SS-topology performance suffers efficiency loss when paired with a higher load, while SP-topology acts differently. This article will observe the best conditions on the selected compensation designs for better application in EV charging systems in a moving state. ABSTRAK: Kenderaan elektrik (EV) telah menarik perhatian dunia sejak pelaksanaan alih kuasa wayarles (WPT) bagi mengecas bateri. Melalui WPT, EV lebih mudah kerana ia boleh dicas secara dinamik. Namun, pengecasan dinamik WPT turut mengalami masalah, seperti mengimbang kecekapan pemindahan kuasa. Beberapa faktor yang membawa kepada masalah ini adalah kerana terdapat gangguan penjajaran dan jarak optimum antara gegelung pemancar dan penerima. Kerana ini, ia menyumbang kepada kehilangan kecekapan kuasa semasa mengecas EV. Pengeluar juga membina spesifikasi stesen pengisian EV berlainan mengikut jenis model EV demi memenuhi permintaan pelanggan. Namun, platform pengecas EV yang berbeza, tidak dapat mengecas EV secara wayarles dengan maksimum. Oleh itu, bagi membaiki keupayaan jana kuasa serta menstabilkan pengeluaran kuasa maksimum semasa proses pengecasan, litar gantian ditambah ke dalam sistem. Artikel ini memberi keutamaan pada dua litar gantian berbeza (topologi bersiri (SS) dan siri-selari (SP)) di bawah aplikasi pengecasan wayarles dinamik. Simulasi dibuat menggunakan NI Multisim mengikut kecekapan pemindahan kuasa dengan frekuensi resonan, pekali gandingan dan rintangan beban. Kecekapan WPT bagi topologi-SP menunjukkan ianya sensitif pada perubahan frekuensi resonan dan pekali gandingan. Manakala topologi-SS kekal cekap semasa proses WPT. Walau bagaimanapun, prestasi topologi-SS berkurangan ketika diganding dengan beban besar, begitu juga berbeza bagi topologi-SP. Artikel ini akan mengkaji keadaan terbaik pada reka bentuk gantian terpilih bagi aplikasi EV dalam sistem pengecasan bergerak.
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2

Wall, Kent D., Christian Gourieroux, and Alain Monfort. "Time Series and Dynamic Models." Journal of the American Statistical Association 93, no. 443 (1998): 1248. http://dx.doi.org/10.2307/2669890.

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3

de Jong, Robert M., and Tiemen Woutersen. "DYNAMIC TIME SERIES BINARY CHOICE." Econometric Theory 27, no. 4 (2011): 673–702. http://dx.doi.org/10.1017/s0266466610000472.

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This paper considers dynamic time series binary choice models. It proves near epoch dependence and strong mixing for the dynamic binary choice model with correlated errors. Using this result, it shows in a time series setting the validity of the dynamic probit likelihood procedure when lags of the dependent binary variable are used as regressors, and it establishes the asymptotic validity of Horowitz’s smoothed maximum score estimation of dynamic binary choice models with lags of the dependent variable as regressors. For the semiparametric model, the latent error is explicitly allowed to be correlated. It turns out that no long-run variance estimator is needed for the validity of the smoothed maximum score procedure in the dynamic time series framework.
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Caby, Errol. "Time Series and Dynamic Models." Technometrics 40, no. 2 (1998): 158. http://dx.doi.org/10.1080/00401706.1998.10485204.

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5

So, Mike K. P., and Ray S. W. Chung. "Dynamic seasonality in time series." Computational Statistics & Data Analysis 70 (February 2014): 212–26. http://dx.doi.org/10.1016/j.csda.2013.09.010.

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6

Solodusha, S. V. "Software Package «Dynamics» for Studying Dynamic Processes by Volterra Series." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 17, no. 2 (2017): 83–92. http://dx.doi.org/10.14529/ctcr170207.

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7

Heitmann, Stewart, and Michael Breakspear. "Putting the “dynamic” back into dynamic functional connectivity." Network Neuroscience 2, no. 2 (2018): 150–74. http://dx.doi.org/10.1162/netn_a_00041.

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The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arises under different configurations of local dynamics and intersystem coupling: We show how they generate time series data with nonlinear and/or nonstationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity, and flexibility. The code for simulating these dynamics is available in a freeware software platform, the Brain Dynamics Toolbox.
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Jardim França, Gleisson, and Braz de Jesus Cardoso Filho. "Series-shunt compensation for harmonic mitigation and dynamic power factor correction." Eletrônica de Potência 17, no. 3 (2012): 641–50. http://dx.doi.org/10.18618/rep.2012.3.641650.

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Li, Peiqiang, Jiang Zhang, Canbing Li, et al. "Dynamic Similar Sub-Series Selection Method for Time Series Forecasting." IEEE Access 6 (2018): 32532–42. http://dx.doi.org/10.1109/access.2018.2843774.

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Borgards, Oliver. "Dynamic time series momentum of cryptocurrencies." North American Journal of Economics and Finance 57 (July 2021): 101428. http://dx.doi.org/10.1016/j.najef.2021.101428.

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Chen, Hao, Yu Xia, Yuekai Pan, and Qing Yang. "Time-series data dynamic density clustering." Intelligent Data Analysis 25, no. 6 (2021): 1487–506. http://dx.doi.org/10.3233/ida-205459.

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In many clustering problems, the whole data is not always static. Over time, part of it is likely to be changed, such as updated, erased, etc. Suffer this effect, the timeline can be divided into multiple time segments. And, the data at each time slice is static. Then, the data along the timeline shows a series of dynamic intermediate states. The union set of data from all time slices is called the time-series data. Obviously, the traditional clustering process does not apply directly to the time-series data. Meanwhile, repeating the clustering process at every time slices costs tremendous. In this paper, we analyze the transition rules of the data set and cluster structure when the time slice shifts to the next. We find there is a distinct correlation of data set and succession of cluster structure between two adjacent ones, which means we can use it to reduce the cost of the whole clustering process. Inspired by it, we propose a dynamic density clustering method (DDC) for time-series data. In the simulations, we choose 6 representative problems to construct the time-series data for testing DDC. The results show DDC can get high accuracy results for all 6 problems while reducing the overall cost markedly.
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Warren, William G., and David C. LeBlanc. "Dynamic standardization of tree-ring series." Canadian Journal of Forest Research 20, no. 9 (1990): 1422–31. http://dx.doi.org/10.1139/x90-188.

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The compound growth function of Warren (W.G. Warren. 1980. Tree-Ring Bull. 40: 35–44) represented an attempt at developing a model-based approach that standardized tree ring width sequences and was more flexible than the monotonic functions that were then commonly used. While the idea was conceptually attractive, operational difficulties of fitting hindered its use as a practical tool. This paper describes the modifications, and what are believed to be improvements, that have recently been made to the method, and which have led to an interactive computer program by which the fitting of the model to any sequence of ring widths may be readily accomplished. The approach also permits the location of both positive and negative departures from a trend (releases and suppressions, respectively) and estimation of the rate of ring-width increase or decrease. For illustration it has been applied to a sample of red spruce (Picearubens Sarg.) cores from Whiteface Mountain, New York. Some synchrony in releases and suppressions was detected, with suppressions being very conspicuous since the late 1950s. Also, the recent reductions in growth rates are as great, or greater, than those previously exhibited by the trees of this sample.
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HIMAJA, K., T. S. SURENDRA, and KALYANI S. TARA. "DYNAMIC MODELING OF SERIES VECTORIAL COMPENSATOR." i-manager’s Journal on Electrical Engineering 12, no. 1 (2018): 18. http://dx.doi.org/10.26634/jee.12.1.14273.

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Abegaz, Fentaw, and U. V. Naik-Nimbalkar. "Dynamic Copula-Based Markov Time Series." Communications in Statistics - Theory and Methods 37, no. 15 (2008): 2447–60. http://dx.doi.org/10.1080/03610920801931846.

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15

Muscatelli, Vito Antonio, and Stan Hurn. "COINTEGRATION AND DYNAMIC TIME SERIES MODELS." Journal of Economic Surveys 6, no. 1 (1992): 1–43. http://dx.doi.org/10.1111/j.1467-6419.1992.tb00142.x.

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16

Pagan, Adrian. "TIME SERIES BEHAVIOUR AND DYNAMIC SPECIFICATION*." Oxford Bulletin of Economics and Statistics 47, no. 3 (2009): 199–211. http://dx.doi.org/10.1111/j.1468-0084.1985.mp47003002.x.

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17

Krampe, Jonas. "Time series modeling on dynamic networks." Electronic Journal of Statistics 13, no. 2 (2019): 4945–76. http://dx.doi.org/10.1214/19-ejs1642.

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18

Yan, Yu-Wei, Yuan Jiang, Song-Qing Yang, Rong-Bin Yu, and Cheng Hong. "Network failure model based on time series." Acta Physica Sinica 71, no. 8 (2022): 088901. http://dx.doi.org/10.7498/aps.71.20212106.

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With the development of network science, the static network has been unable to clearly characterize the dynamic process of the network. In real networks, the interaction between individuals evolves rapidly over time. This network model closely links time to interaction process. Compared with static networks, dynamic networks can clearly describe the interaction time of nodes, which has more practical significance. Therefore, how to better describe the behavior changes of networks after being attacked based on time series is an important problem in the existing cascade failure research. In order to better answer this question, a failure model based on time series is proposed in this paper. The model is constructed according to time, activation ratio, number of edges and connection probability. By randomly attacking nodes at a certain time, the effects of four parameters on sequential networks are analyzed. In order to validate the validity and scientificity of this failure model, we use small social networks in the United States. The experimental results show that the model is feasible. The model takes into account the time as well as the spreading dynamics and provides a reference for explaining the dynamic networks in reality.
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19

Zhang, Yao Man, Ren Jun Gu, and Guang Xiao Yang. "Static and Dynamic Characteristic Analysis for the HSR Series Linear Motion Guide." Advanced Materials Research 834-836 (October 2013): 1488–92. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.1488.

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The dynamic characteristic of junction surface is an important part of the dynamic characteristics of NC machine tools, and it is sometimes the weakest link of the machine tool's dynamic characteristics. The precision of the finite element calculation of the machine tool's dynamic characteristics shall be affected by the junction surface parameters directly. Linear Motion guide is one of the most widely used components of the NC machine tool, and its characteristic is one of the most important components of the performances of the NC machine tool. The HSR series of linear motion guides are taken as the object of the paper, and its static and dynamic characteristics are studied. And the parameters of static and dynamic characteristics of HSR, such as static stiffness, natural frequency and modal shape are obtained. The accuracy of the finite element analysis mode is verified, which establishes the foundation for the dynamics analysis of machine tool.
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20

Szántó, Attila, János Kiss, Tamás Mankovits, and Gusztáv Áron Szíki. "Dynamic Test Measurements and Simulation on a Series Wound DC Motor." Applied Sciences 11, no. 10 (2021): 4542. http://dx.doi.org/10.3390/app11104542.

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Previously, a dynamic model and a simulation program for series wound DC motors (SWDCM) were developed in MATLAB/Simulink for modelling them in electric vehicles and mechatronic systems. The electromagnetic characteristics of the motor (electric resistances, dynamic inductances), which serve as input parameters of the program, were also measured. Additionally, locked rotor response measurements were performed to test the accuracy of the measured electromagnetic characteristics. This paper presents the experimental procedure and the results of dynamics test measurements that were performed on the same motor, including the procedure for the determination of the necessary input dynamic parameters for the simulation. While the motor spins up from rest, the intensity of the electric current and the angular speed of the rotor are measured. Finally, the simulation and dynamic test results are compared to check the proper operation of the simulation program.
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21

Sidanchenko, V. V., and O. Yu Gusev. "Research on stochastic properties of time series data on chemical analysis of cast iron." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 4 (August 30, 2024): 135–40. http://dx.doi.org/10.33271/nvngu/2024-4/135.

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Purpose. To provide a procedure for identifying chaotic processes in a dynamic system and to examine time series, describing the chemical composition of cast iron at the blast furnace output with the purpose of identifying the nonlinearity of the investigated system and detecting the presence of chaotic processes in it. Methodology. The determination of the unique characteristics of the attractor of a dynamic chaotic system based on the time series of cast iron’s chemical composition values was carried out using methods of nonlinear dynamics and dynamic chaos theory, such as the autocorrelation function method, correlation and fractal dimensions. Findings. The methods of nonlinear dynamics and dynamic chaos theory were used to study the behavior of time series data on the chemical composition of cast iron at the blast furnace output. The presence was identified of chaotic processes with a fractal structure in the studied dynamic system, leading to the inefficiency of traditional analysis methods based on the Gaussian properties of stochastic processes. Originality. For the first time, the possibility and feasibility of applying chaos theory methods for the analysis and prediction of time series data on the chemical composition of cast iron at the blast furnace output were substantiated. For the first time, the nonlinearity of the studied dynamic system was identified, and chaotic processes were discovered within it by determining the unique characteristics of the strange attractor of the system using the analyzed time series, such as embedding dimension, time delay, and the largest Lyapunov exponent. Practical value. The obtained results open up the possibility for more effective and qualitative analysis of the behavior of the studied dynamic system by developing new tools for assessment and prediction that are adequate to the nature of the ongoing processes.
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Xie, Wen-Jie, Rui-Qi Han, and Wei-Xing Zhou. "Time series classification based on triadic time series motifs." International Journal of Modern Physics B 33, no. 21 (2019): 1950237. http://dx.doi.org/10.1142/s0217979219502370.

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It is of great significance to identify the characteristics of time series to quantify their similarity and classify different classes of time series. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from the time series. Based on triadic time series motif profiles, we further propose to estimate the similarity coefficients between different time series and classify these time series with high accuracy. We validate the method with time series generated from nonlinear dynamic systems (logistic map, chaotic logistic map, chaotic Henon map, chaotic Ikeda map, hyperchaotic generalized Henon map and hyperchaotic folded-tower map) and retrieved from the UCR Time Series Classification Archive. Our analysis shows that the proposed triadic time series motif analysis performs better than the classic dynamic time wrapping method in classifying time series for certain datasets investigated in this work.
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Lasisi. "Specification of Dynamic Time Series Model with Volatile-Outlier Input Series." American Journal of Applied Sciences 8, no. 11 (2011): 1149–53. http://dx.doi.org/10.3844/ajassp.2011.1149.1153.

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24

Ma, Qianli, Wanqing Zhuang, Sen Li, Desen Huang, and Garrison Cottrell. "Adversarial Dynamic Shapelet Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5069–76. http://dx.doi.org/10.1609/aaai.v34i04.5948.

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Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly. Although learning-based shapelet methods achieve better results than previous methods, they still have two shortcomings. First, the learned shapelets are fixed after training and cannot adapt to time series with deformations at the testing phase. Second, the shapelets learned by back-propagation may not be similar to any real subsequences, which is contrary to the original intention of shapelets and reduces model interpretability. In this paper, we propose a novel shapelet learning model called Adversarial Dynamic Shapelet Networks (ADSNs). An adversarial training strategy is employed to prevent the generated shapelets from diverging from the actual subsequences of a time series. During inference, a shapelet generator produces sample-specific shapelets, and a dynamic shapelet transformation uses the generated shapelets to extract discriminative features. Thus, ADSN can dynamically generate shapelets that are similar to the real subsequences rather than having arbitrary shapes. The proposed model has high modeling flexibility while retaining the interpretability of shapelet-based methods. Experiments conducted on extensive time series data sets show that ADSN is state-of-the-art compared to existing shapelet-based methods. The visualization analysis also shows the effectiveness of dynamic shapelet generation and adversarial training.
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Ahmad, Noor Atinah, and Nurul Ashikin Othman. "Strategies for Producing Reliable Trends Forecasting of COVID-19 Pandemic in Malaysia using Dynamic Mode Decomposition." Chiang Mai Journal of Science 50, no. 3 (2023): 1–14. http://dx.doi.org/10.12982/cmjs.2023.026.

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Dynamic Mode Decomposition (DMD) with time delay embedding is used to predict dynamic patterns in univariate time series. An important pattern that can be extracted using DMD is the trend or global change in a time series which is useful for producing reliable forecast. DMD utilizes the computationally effi cient singular value decomposition (SVD) to produce a low rank approximation of the linear operator that brings about the dynamic patterns in the time series. Trend in the time series is translated as dynamic modes of the operator with low frequencies. The time evolution of this low frequency pattern produces forecast of the time series. In this paper, we outline the strategies for extracting trend component from COVID-19 time series of Malaysia. It is discovered that, other than identifying modes with slow varying frequencies, we need to also resolve the time stamp delay, so that mean-square error of the reconstructed time series is minimal. Information of the magnitude and phase of DMD modes are useful to identify persistent patterns and remove nonstationary ones. We compare the performance of DMD with another SVD-based method which is the singular spectrum analysis (SSA) and our results highlight certain fundamental difference between these two methods. The forecasts from SSA tend to lean towards the direction of maximum variance, producing low reconstruction error but slow to detect sudden changes in the time series. On the other hand, forecasts from DMD captures the phases of dominant modes that dictates the overall global pattern, hence providing a better prediction of future dynamics of the time series.
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Kulkarni, D. R., J. C. Parikh, and A. S. Pandya. "Dynamic Predictions from Time Series Data — An Artificial Neural Network Approach." International Journal of Modern Physics C 08, no. 06 (1997): 1345–60. http://dx.doi.org/10.1142/s0129183197001193.

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A hybrid approach, incorporating concepts of nonlinear dynamics in artificial neural networks (ANN), is proposed to model a time series generated by complex dynamic systems. We introduce well-known features used in the study of dynamic systems — time delay τ and embedding dimension d — for ANN modeling of time series. These features provide a theoretical basis for selecting the optimal size for the number of neurons in the input layer. The main outcome of the new approach for such problems is that to a large extent it defines the ANN architecture, models the time series and gives good prediction. As a consequence, we have an integrated and systematic data-driven scheme for modeling time series data. We illustrate our method by considering computer generated periodic and chaotic time series. The ANN model developed gave excellent quality of fit for the training and test sets as well as for iterative dynamic predictions for future values of the two time series. Further, computer experiments were conducted by introducing Gaussian noise of various degrees in the two time series, to simulate real world effects. We find that up to a limit introduction of noise leads to a smaller network with good generalizing capability.
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Chen, Jie, Yangjun Wu, Xiaolong He, Limin Zhang, and Shijie Dong. "Suspension parameter design of underframe equipment considering series stiffness of shock absorber." Advances in Mechanical Engineering 12, no. 5 (2020): 168781402092264. http://dx.doi.org/10.1177/1687814020922647.

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In this article, a vertical rigid–flexible coupling model between the vehicle and the equipment is established. Considering the series stiffness of hydraulic shock absorbers, the underframe equipment is like a three-element-type Maxwell model dynamic vibration absorber. The carbody is approximated by an elastic beam and the three-element-type dynamic vibration absorber for general beam system was studied by fixed-point theory. The analytical solution of the optimal suspension parameters for the beam system subjected to harmonic excitation is obtained. The dynamic vibration absorber theory is applied to reduce the resonance of the carbody and to design the suspension parameters of the underframe equipment accordingly. Then, the railway vehicle model was established by multi-body dynamics simulation software, and the vibration levels of the vehicle at different speeds were calculated. A comparative analysis was made between the vehicles whose underframe equipment was suspended by the three-element-type dynamic vibration absorber model and the Kelvin–Voigt-type dynamic vibration absorber model, respectively. The results show that, compared with the vehicle whose underframe equipment is suspended by the Kelvin–Voigt-type dynamic vibration absorber model, the vehicle whose underframe equipment is suspended by the three-element-type dynamic vibration absorber model can achieve a much better ride quality and root mean square value of the vibration acceleration of the carbody. The carbody elastic vibration can be reduced and the vehicle ride quality can be improved effectively using the designed absorber.
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Krener, Arthur J. "Series Solution of Stochastic Dynamic Programming Equations." IFAC-PapersOnLine 53, no. 2 (2020): 2165–70. http://dx.doi.org/10.1016/j.ifacol.2020.12.2544.

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PATTERSON, K. D. "GROWTH COEFFICIENTS IN DYNAMIC TIME SERIES MODELS." Oxford Economic Papers 39, no. 2 (1987): 282–92. http://dx.doi.org/10.1093/oxfordjournals.oep.a041786.

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Matteson, David S., and Ruey S. Tsay. "Dynamic Orthogonal Components for Multivariate Time Series." Journal of the American Statistical Association 106, no. 496 (2011): 1450–63. http://dx.doi.org/10.1198/jasa.2011.tm10616.

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Kejun Li, Jianguo Zhao, Chenghui Zhang, and Wei-Jen Lee. "Dynamic Simulator for Thyristor-Controlled Series Capacitor." IEEE Transactions on Industry Applications 46, no. 3 (2010): 1096–102. http://dx.doi.org/10.1109/tia.2010.2046283.

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Sengupta, Jati K. "Dynamic Farrell efficiency: a time series application." Applied Economics Letters 2, no. 10 (1995): 363–66. http://dx.doi.org/10.1080/758518990.

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Feng, Xia-ting, M. Seto, and K. Katsuyama. "Neural dynamic modelling on earthquake magnitude series." Geophysical Journal International 128, no. 3 (1997): 547–56. http://dx.doi.org/10.1111/j.1365-246x.1997.tb05317.x.

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Francisco, Gerson, Cláudio Paiva, and Rogerio Rosenfeld. "Dynamic parameters for Brazilian financial time series." Economia Aplicada 6, no. 1 (2002): 67–77. https://doi.org/10.11606/1413-8050/ea219890.

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In this study we analyse a Brazilian stock index called IBOVESPA using techniques from dynamical systems theory and stochastic processes. We discuss the Lyapunov exponent, the correlation dimension, the LempelZiv complexity, the Hurst exponent and the BDS statistics. We compare this study with other time series including stock prices and deterministic systems. We conclude that the IBOVESPA is a linear stochastic process that exhibits the phenomenon of persistence, thatis, it has long term memory. The stocks are described by nonlinear stochastic processes making it impossible to be simulated with deterministic models such as the usual neural networks architectures.
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Russo, Patrice, Firas Yengui, Gael Pillonnet, Sophie Taupin, and Nacer Abouchi. "Dynamic voltage scaling for series hybrid amplifiers." Microelectronics Journal 44, no. 9 (2013): 753–63. http://dx.doi.org/10.1016/j.mejo.2013.03.016.

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ODOLPHIN, E. J. G., and S. E. JOHNSON. "Decomposition of Time Series Dynamic Linear Models." Journal of Time Series Analysis 24, no. 5 (2003): 513–27. http://dx.doi.org/10.1111/1467-9892.00319.

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Shang, Han Lin, and Rob J. Hyndman. "Nonparametric time series forecasting with dynamic updating." Mathematics and Computers in Simulation 81, no. 7 (2011): 1310–24. http://dx.doi.org/10.1016/j.matcom.2010.04.027.

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Fedosova, V. I., and O. V. Yaremenko. "Unification of dimensional series of dynamic pumps." Chemical and Petroleum Engineering 25, no. 2 (1989): 59–62. http://dx.doi.org/10.1007/bf01245842.

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Dagum, Estela Bee, and Benoît Quenneville. "Dynamic linear models for time series components." Journal of Econometrics 55, no. 1-2 (1993): 333–51. http://dx.doi.org/10.1016/0304-4076(93)90020-6.

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Findley, David F. "Dynamic linear models for time series components." Journal of Econometrics 55, no. 1-2 (1993): 353–56. http://dx.doi.org/10.1016/0304-4076(93)90021-v.

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41

Buhlmann, P. "Dynamic adaptive partitioning for nonlinear time series." Biometrika 86, no. 3 (1999): 555–71. http://dx.doi.org/10.1093/biomet/86.3.555.

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42

Brida, Juan G., and Lionello F. Punzo. "Symbolic time series analysis and dynamic regimes." Structural Change and Economic Dynamics 14, no. 2 (2003): 159–83. http://dx.doi.org/10.1016/s0954-349x(02)00050-4.

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43

Zan, Chaw Thet, and Hayato Yamana. "Dynamic SAX parameter estimation for time series." International Journal of Web Information Systems 13, no. 4 (2017): 387–404. http://dx.doi.org/10.1108/ijwis-04-2017-0035.

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Purpose The paper aims to estimate the segment size and alphabet size of Symbolic Aggregate approXimation (SAX). In SAX, time series data are divided into a set of equal-sized segments. Each segment is represented by its mean value and mapped with an alphabet, where the number of adopted symbols is called alphabet size. Both parameters control data compression ratio and accuracy of time series mining tasks. Besides, optimal parameters selection highly depends on different application and data sets. In fact, these parameters are iteratively selected by analyzing entire data sets, which limits handling of the huge amount of time series and reduces the applicability of SAX. Design/methodology/approach The segment size is estimated based on Shannon sampling theorem (autoSAXSD_S) and adaptive hierarchical segmentation (autoSAXSD_M). As for the alphabet size, it is focused on how mean values of all the segments are distributed. The small number of alphabet size is set for large distribution to easily distinguish the difference among segments. Findings Experimental evaluation using University of California Riverside (UCR) data sets shows that the proposed schemes are able to select the parameters well with high classification accuracy and show comparable efficiency in comparison with state-of-the-art methods, SAX and auto_iSAX. Originality/value The originality of this paper is the way to find out the optimal parameters of SAX using the proposed estimation schemes. The first parameter segment size is automatically estimated on two approaches and the second parameter alphabet size is estimated on the most frequent average (mean) value among segments.
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Xiao, Qinkun, Chu Chaoqin, and Zhao Li. "Time series prediction using dynamic Bayesian network." Optik 135 (April 2017): 98–103. http://dx.doi.org/10.1016/j.ijleo.2017.01.073.

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Moo, C. S., Y. C. Hsieh, I. S. Tsai, and J. C. Cheng. "Dynamic charge equalisation for series-connected batteries." IEE Proceedings - Electric Power Applications 150, no. 5 (2003): 501. http://dx.doi.org/10.1049/ip-epa:20030661.

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Ogras, Umit Y., and Hakan Ferhatosmanoglu. "Online summarization of dynamic time series data." VLDB Journal 15, no. 1 (2005): 84–98. http://dx.doi.org/10.1007/s00778-004-0149-x.

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David, S. A., D. D. Quintino, C. M. C. Inacio, and J. A. T. Machado. "Fractional dynamic behavior in ethanol prices series." Journal of Computational and Applied Mathematics 339 (September 2018): 85–93. http://dx.doi.org/10.1016/j.cam.2018.01.007.

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Bokil, Hemant, Ofer Tchernichovsky, and Partha P. Mitra. "Dynamic Phenotypes: Time Series Analysis Techniques for Characterizing Neuronal and Behavioral Dynamics." Neuroinformatics 4, no. 1 (2006): 119–28. http://dx.doi.org/10.1385/ni:4:1:119.

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Rigat, Fabio, and Jim Q. Smith. "Semi-parametric dynamic time series modelling with applications to detecting neural dynamics." Annals of Applied Statistics 3, no. 4 (2009): 1776–804. http://dx.doi.org/10.1214/09-aoas275.

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Ernst, Anja F., Casper J. Albers, Bertus F. Jeronimus, and Marieke E. Timmerman. "Inter-Individual Differences in Multivariate Time-Series." European Journal of Psychological Assessment 36, no. 3 (2020): 482–91. http://dx.doi.org/10.1027/1015-5759/a000578.

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Abstract. Theories of emotion regulation posit the existence of individual differences in emotion dynamics. Current multi-subject time-series models account for differences in dynamics across individuals only to a very limited extent. This results in an aggregation that may poorly apply at the individual level. We present the exploratory method of latent class vector-autoregressive modeling (LCVAR), which extends the time-series models to include clustering of individuals with similar dynamic processes. LCVAR can identify individuals with similar emotion dynamics in intensive time-series, which may be of unequal length. The method performs excellently under a range of simulated conditions. The value of identifying clusters in time-series is illustrated using affect measures of 410 individuals, assessed at over 70 time points per individual. LCVAR discerned six clusters of distinct emotion dynamics with regard to diurnal patterns and augmentation and blunting processes between eight emotions.
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