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Journal articles on the topic 'Time-varying Efficiency'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Marjai, Péter, and Attila Kiss. "Efficiency centrality in time-varying graphs." Acta Universitatis Sapientiae, Informatica 12, no. 1 (July 1, 2020): 5–21. http://dx.doi.org/10.2478/ausi-2020-0001.

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AbstractOne of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is cost-effiective and easy to calculate, although using global metrics like betweenness centrality or closeness centrality can identify influential nodes more accurately, however calculating these values can be costly and each measure has it’s own limitations and disadvantages. There is an ever-growing interest in calculating such metrics in time-varying graphs (TVGs), since modern complex networks can be best modelled with such graphs. In this paper we are investigating the effectiveness of a new centrality measure called efficiency centrality in TVGs. To evaluate the performance of the algorithm Independent Cascade Model is used to simulate infection spreading in four real networks. To simulate the changes in the network we are deleting and adding nodes based on their degree centrality. We are investigating the Time-Constrained Coverage and the magnitude of propagation resulted by the use of the algorithm.

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2

Attanasio, Orazio P. "Risk, Time-Varying Second Moments and Market Efficiency." Review of Economic Studies 58, no. 3 (May 1991): 479. http://dx.doi.org/10.2307/2298007.

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3

Ge, Tingting. "Time-varying transmission efficiency of China’s monetary policy." China Economic Journal 12, no. 1 (December 17, 2018): 32–51. http://dx.doi.org/10.1080/17538963.2018.1556421.

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4

Charfeddine, Lanouar, and Karim Ben Khediri. "Time varying market efficiency of the GCC stock markets." Physica A: Statistical Mechanics and its Applications 444 (February 2016): 487–504. http://dx.doi.org/10.1016/j.physa.2015.09.063.

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5

Fazlollahi, Negar, Nesrin Ozatac, and Korhan K. Gokmenoglu. "Evolving time-varying market efficiency of energy stock market." Environmental Science and Pollution Research 27, no. 36 (August 15, 2020): 45539–54. http://dx.doi.org/10.1007/s11356-020-09887-7.

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6

Parshakov, Petr. "Observing unobservable: estimating the time-varying efficiency of intellectual capital." Measuring Business Excellence 21, no. 4 (November 20, 2017): 377–87. http://dx.doi.org/10.1108/mbe-05-2017-0024.

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Purpose Company intellectual capital (IC) is nowadays considered as a key resource that can transform a company’s value. For this reason, the efficiency of IC is crucial for all stakeholders. Evaluating efficiency is difficult, because IC is partly unobservable and its efficiency varies across time. The aim of this study is to suggest a methodology for estimating the dynamic efficiency of a company’s intellectual resources. Design/methodology/approach The panel data model suggested by Kneip et al. (2012) is used to estimate dynamic efficiency. The main feature of this model is that the unobservable component has a multi-dimensional factor structure. Taking advantage of the ability of this model to control for unobserved complex heterogeneity, the authors use the results in further stochastic frontier analysis. A data set containing information about Russian companies for the period from 2001 to 2010 is used. Findings In this paper, the dynamic efficiency of Russian companies is estimated. It is shown that, using the traditional efficiency estimate, companies can be overestimated. Research limitations/implications The main limitation of the suggested methodology is that it is necessary to have a long panel data structure. Practical implications Taking advantage of time-varying efficiency, one can estimate the efficiency growth rate as a measure of performance, standard deviation as a measure of risk and autocorrelation as a measure of stability. Originality/value This is the first study to present clear evidence of the time-varying nature of IC efficiency. On the methodological side, the paper presents a fairly simple method capable of estimating various indicators of a company’s efficiency.

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7

Henningsson, Per, and Richard J. Bomphrey. "Time-varying span efficiency through the wingbeat of desert locusts." Journal of The Royal Society Interface 9, no. 71 (November 23, 2011): 1177–86. http://dx.doi.org/10.1098/rsif.2011.0749.

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The flight performance of animals depends greatly on the efficacy with which they generate aerodynamic forces. Accordingly, maximum range, load-lifting capacity and peak accelerations during manoeuvres are all constrained by the efficiency of momentum transfer to the wake. Here, we use high-speed particle image velocimetry (1 kHz) to record flow velocities in the near wake of desert locusts ( Schistocerca gregaria , Forskål). We use the measured flow fields to calculate time-varying span efficiency throughout the wing stroke cycle. The locusts are found to operate at a maximum span efficiency of 79 per cent, typically at a plateau of about 60 per cent for the majority of the downstroke, but at lower values during the upstroke. Moreover, the calculated span efficiencies are highest when the largest lift forces are being generated (90% of the total lift is generated during the plateau of span efficiency) suggesting that the combination of wing kinematics and morphology in locust flight perform most efficiently when doing the most work.

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8

Desli, Evangelia, Subhash C. Ray, and Subal C. Kumbhakar. "A dynamic stochastic frontier production model with time-varying efficiency." Applied Economics Letters 10, no. 10 (August 2003): 623–26. http://dx.doi.org/10.1080/1350485032000133291.

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9

Shi, Peng, and Wei Zhang. "Time-varying X-efficiency: evidence from US property casualty insurers." Applied Economics Letters 18, no. 3 (February 2011): 217–21. http://dx.doi.org/10.1080/13504850903559559.

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10

Ding, Mengying, and Yali Dong. "Robust Finite-time Boundedness of Discrete-time Neural Networks with Time-varying Delays." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 17 (February 23, 2021): 146–55. http://dx.doi.org/10.37394/23209.2020.17.18.

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This paper is concerned with the problem of robust finite-time boundedness for the discrete-time neural networks with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii functional, we propose the sufficient conditions which ensure the robust finite-time boundedness of the discrete-time neural networks with time-varying delay in terms of linear matrix inequalities. Then the sufficient conditions of robust finite-time stability for the discrete-time neural networks with time-varying delays are given. Finally, a numerical example is presented to illustrate the efficiency of proposed methods.

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11

Hu, Jin-Li, and Tzu-Pu Chang. "REGIONAL RECYCLING EFFICIENCY IN TAIWAN WITH TIME-VARYING EFFECTS OF DETERMINANTS." Journal of Solid Waste Technology and Management 37, no. 2 (May 1, 2011): 83–90. http://dx.doi.org/10.5276/jswtm.2011.83.

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12

Nguyen, Anne, and Jean-Jacques Greffet. "Efficiency optimization of mid-infrared incandescent sources with time-varying temperature." Optical Materials Express 12, no. 1 (December 21, 2021): 225. http://dx.doi.org/10.1364/ome.443129.

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13

Wang, Chunhua. "A dynamic stochastic frontier production model with time-varying efficiency: comment." Applied Economics Letters 14, no. 6 (May 2007): 415–17. http://dx.doi.org/10.1080/13504850500447455.

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14

Ferreira, Paulo. "Efficiency or speculation? A time-varying analysis of European sovereign debt." Physica A: Statistical Mechanics and its Applications 490 (January 2018): 1295–308. http://dx.doi.org/10.1016/j.physa.2017.08.137.

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15

Yan, Jia, Xinyu Sun, and John J. Liu. "Assessing container operator efficiency with heterogeneous and time-varying production frontiers." Transportation Research Part B: Methodological 43, no. 1 (January 2009): 172–85. http://dx.doi.org/10.1016/j.trb.2008.06.001.

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16

Paul, Samit. "Time Varying Efficiency in Indian Sectors: An Event Study on Demonetization." Journal of Quantitative Economics 18, no. 1 (May 17, 2019): 103–27. http://dx.doi.org/10.1007/s40953-019-00171-1.

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17

Ito, Mikio, Akihiko Noda, and Tatsuma Wada. "International stock market efficiency: a non-Bayesian time-varying model approach." Applied Economics 46, no. 23 (April 22, 2014): 2744–54. http://dx.doi.org/10.1080/00036846.2014.909579.

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18

Wang, Kai, Xinping Yan, Yupeng Yuan, Xiaoli Jiang, Xiao Lin, and Rudy R. Negenborn. "Dynamic optimization of ship energy efficiency considering time-varying environmental factors." Transportation Research Part D: Transport and Environment 62 (July 2018): 685–98. http://dx.doi.org/10.1016/j.trd.2018.04.005.

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19

Festger, Adam D., and Gary R. Walter. "The Capture Efficiency Map: The Capture Zone Under Time-Varying Flow." Ground Water 40, no. 6 (November 2002): 619–28. http://dx.doi.org/10.1111/j.1745-6584.2002.tb02548.x.

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20

Jebabli, Ikram, and David Roubaud. "Time-varying efficiency in food and energy markets: Evidence and implications." Economic Modelling 70 (April 2018): 97–114. http://dx.doi.org/10.1016/j.econmod.2017.10.013.

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21

Kim, Yong-Jin, Jeahoon Cho, and Kyung-Young Jung. "Efficient Finite-Difference Time-Domain Modeling of Time-Varying Dusty Plasma." Journal of Electromagnetic Engineering and Science 22, no. 4 (July 31, 2022): 502–8. http://dx.doi.org/10.26866/jees.2022.4.r.115.

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The finite-difference time-domain (FDTD) method has been widely used for the electromagnetic analysis of dusty plasma sheath in reentering hypersonic vehicles. The time-varying characteristics of dusty plasma should be considered to accurately analyze THz wave propagation in dusty plasma. In this work, we propose an efficient FDTD modeling of time-varying dusty plasma based on the combination of the bilinear transform and the state-space approach. The proposed FDTD formulation for time-varying dusty plasma can lead to a significant improvement in computational efficiency against the conventional shift operator FDTD counterpart while maintaining numerical accuracy. Numerical examples are performed to validate the proposed FDTD modeling of time-varying dusty plasma.

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22

Khuntia, Sashikanta, and J. K. Pattanayak. "Evolving Efficiency of Exchange Rate Movement: An Evidence from Indian Foreign Exchange Market." Global Business Review 21, no. 4 (August 1, 2019): 956–69. http://dx.doi.org/10.1177/0972150919856996.

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This study empirically verifies the evolving and time-varying efficiency of Indian foreign exchange market using the framework of adaptive market hypothesis (AMH). Whether market efficiency is time varying or static, and if time varying, identification of possible events causing such time-varying efficiency are the two major agenda of this study. We employ a set of recent methods which are robust and possess stronger power properties. Moreover, we follow a fixed-length rolling window approach to explore time-varying nature of market efficiency and to avoid data-snooping bias. Our overall findings suggest that market efficiency is not an all-or-nothing condition; it varies over time. We also find that episodes of efficiency coincide with emergence of major events and market microstructure issues. Particularly, changes in exchange rate regime, financial turbulence, major central bank interventions and trade volume are the prominent causes for time-varying efficiency in INR–USD exchange rate. The evidence of swing between efficiency and inefficiency can prompt currency traders to exploit arbitrage opportunities that emerge with different market conditions.

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23

Takaishi, Tetsuya. "Time-varying properties of asymmetric volatility and multifractality in Bitcoin." PLOS ONE 16, no. 2 (February 1, 2021): e0246209. http://dx.doi.org/10.1371/journal.pone.0246209.

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This study investigates the volatility of daily Bitcoin returns and multifractal properties of the Bitcoin market by employing the rolling window method and examines relationships between the volatility asymmetry and market efficiency. Whilst we find an inverted asymmetry in the volatility of Bitcoin, its magnitude changes over time, and recently, it has become small. This asymmetric pattern of volatility also exists in higher frequency returns. Other measurements, such as kurtosis, skewness, average, serial correlation, and multifractal degree, also change over time. Thus, we argue that properties of the Bitcoin market are mostly time dependent. We examine efficiency-related measures: the Hurst exponent, multifractal degree, and kurtosis. We find that when these measures represent that the market is more efficient, the volatility asymmetry weakens. For the recent Bitcoin market, both efficiency-related measures and the volatility asymmetry prove that the market becomes more efficient.

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24

Mehrara, Mohsen, and Monire Hamldar. "Time-Varying Optimal Hedge Ratio for Brent Oil Market." International Letters of Social and Humanistic Sciences 56 (July 2015): 103–6. http://dx.doi.org/10.18052/www.scipress.com/ilshs.56.103.

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This paper examines the optimal hedging ratio (OHR) for the Brent Crude Oil Futures using daily data over the period 1990/17/8-2014/11/3. To estimate OHR, we employ multivariate BEKK MV-GARCH model. At last, the efficiency of this approach are compared with the constant OHR captured from OLS through Edrington's index.

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25

Bonga-Bonga, Lumengo. "The Evolving Efficiency Of The South African Stock Exchange." International Business & Economics Research Journal (IBER) 11, no. 9 (August 17, 2012): 997. http://dx.doi.org/10.19030/iber.v11i9.7183.

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This paper tests the weak-form efficiency in the South African stock exchange - the Johannesburg Securities Exchange (JSE) - under the hypothesis that emerging markets efficiency evolves through time as these markets constantly enhance their regulatory environment. The paper makes use of the time varying GARCH model in testing this hypothesis. In addition, the paper compares the out-of-sample forecast performance of the time varying and fixed parameter GARCH models in predicting stock returns in the JSE making use of MSE-F statistics for nested models proposed (McCracken, 1999). The findings of the paper show that the two models provide the same conclusion in showing that the JSE has been efficient during the period of the analysis. In addition, the time varying model outperforms the fixed coefficient model in predicting the JSE stock returns. This finding indicates that the time-varying parameter model adds a benefit in testing the weak-form efficiency or modelling stock return in the JSE.

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26

Shattock, Andrew J., Cliff C. Kerr, Robyn M. Stuart, Emiko Masaki, Nicole Fraser, Clemens Benedikt, Marelize Gorgens, David P. Wilson, and Richard T. Gray. "In the interests of time: improving HIV allocative efficiency modelling via optimal time-varying allocations." Journal of the International AIDS Society 19, no. 1 (January 2016): 20627. http://dx.doi.org/10.7448/ias.19.1.20627.

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27

Eheart, J. Wayland, E. Downey Brill, Barbara J. Lence, John D. Kilgore, and James G. Uber. "Cost efficiency of time-varying discharge permit programs for water quality management." Water Resources Research 23, no. 2 (February 1987): 245–51. http://dx.doi.org/10.1029/wr023i002p00245.

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28

Zheng, Lingxiao, Tyson L. Hedrick, and Rajat Mittal. "Time-Varying Wing-Twist Improves Aerodynamic Efficiency of Forward Flight in Butterflies." PLoS ONE 8, no. 1 (January 16, 2013): e53060. http://dx.doi.org/10.1371/journal.pone.0053060.

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29

Li, Xiao-Ming. "Time-varying Informational Efficiency in China's A-Share and B-Share Markets." Journal of Chinese Economic and Business Studies 1, no. 1 (January 2003): 33–56. http://dx.doi.org/10.1080/1476528032000039730.

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30

Zhu, D., R. P. Broadwater, K. Tam, R. Seguin, and H. Asgeirsson. "Impact of DG Placement on Reliability and Efficiency With Time-Varying Loads." IEEE Transactions on Power Systems 21, no. 1 (February 2006): 419–27. http://dx.doi.org/10.1109/tpwrs.2005.860943.

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31

Zhang, Wei, Hong Ma, Tao Wu, Xueshu Shi, and Yiwen Jiao. "Efficient topology control for time-varying spacecraft networks with unreliable links." International Journal of Distributed Sensor Networks 15, no. 9 (September 2019): 155014771987937. http://dx.doi.org/10.1177/1550147719879377.

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In spacecraft networks, the time-varying topology, intermittent connectivity, and unreliable links make management of the network challenging. Previous works mainly focus on information propagation or routing. However, with a large number of nodes in the future spacecraft networks, it is very crucial regarding how to make efficient network topology controls. In this article, we investigate the topology control problem in spacecraft networks where the time-varying topology can be predicted. We first develop a model that formalizes the time-varying spacecraft network topologies as a directed space–time graph. Compared with most existing static graph models, this model includes both temporal and spatial topology information. To capture the characteristics of practical network, links in our space–time graph model are weighted by cost, efficiency, and unreliability. The purpose of our topology control is to construct a sparse (low total cost) structure from the original topology such that (1) the topology is still connected over space–time graph; (2) the cost efficiency ratio of the topology is minimized; and (3) the unreliability parameter is lower than the required bound. We prove that such an optimization problem is NP-hard. Then, we provide five heuristic algorithms, which can significantly maintain low topology cost efficiency ratio while achieving high reliable connectivity. Finally, simulations have been conducted on random space networks and hybrid low earth orbit/geostationary earth orbit satellite-based sensor network. Simulation results demonstrate the efficiency of our model and topology control algorithms.

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32

Yildirim, Mehmet Ali, and Marzena Nowak-Ocłoń. "Modified Maximum Power Point Tracking Algorithm under Time-Varying Solar Irradiation." Energies 13, no. 24 (December 20, 2020): 6722. http://dx.doi.org/10.3390/en13246722.

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Solar photovoltaic (PV) energy is one of the most viable renewable energy sources, considered less polluting than fossil energy. However, the average power conversion efficiency of PV systems is between 15% and 20%, and they must operate with high efficiency. Photovoltaic cells have non-linear voltage–current characteristics that are dependent on environmental factors such as solar irradiation and temperature, and have low efficiency. Therefore, it becomes crucial to harvest the maximum power from PV panels. This paper aims to study and analyze the most common and well-known maximum power point tracking (MPPT) algorithms, perturb and observe (P&O) and incremental conductance (IncCond). These algorithms were found to be easy to implement, low-cost techniques suitable for large- and medium-sized photovoltaic applications. The algorithms were tested and compared dynamically using MATLAB/Simulink software. In order to overcome the low performance of the P&O and IncCond methods under time-varying and fast-changing solar irradiation, several modifications are proposed. Results show an improvement in the tracking and overall system efficiencies and a shortened response time compared with original techniques. In addition, the proposed algorithms minimize the oscillations around the maximum power point (MPP), and the power converges faster.

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33

Yang, Qiongfen. "Delay-Dependent Finite-Time andL2-Gain Analysis for Switched Systems with Time-Varying Delay." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/594340.

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For the switched systems, switching behavior always affects the finite-time stability (FTS) property, which was neglected by most previous studies. This paper is mainly concerned with the problem of delay-dependent finite-time andL2-gain analysis for switched systems with time-varying delay. Several less conservative sufficient conditions related to finite-time stability and boundness of switched system with time-varying delays are proposed; the system trajectory stays within a special bound with the information of switching signal. At last, a numerical example is also given to illustrate the efficiency of the developed method.

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34

Li, Yajun. "Exponential Stability Results of Discrete-Time Stochastic Neural Networks with Time-Varying Delays." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/486257.

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An innovative stability analysis approach for a class of discrete-time stochastic neural networks (DSNNs) with time-varying delays is developed. By constructing a novel piecewise Lyapunov-Krasovskii functional candidate, a new sum inequality is presented to deal with sum items without ignoring any useful items, the model transformation is no longer needed, and the free weighting matrices are added to reduce the conservatism in the derivation of our results, so the improvement of computational efficiency can be expected. Numerical examples and simulations are also given to show the effectiveness and less conservatism of the proposed criteria.

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35

Zhao, Ye Lei. "Iterative Learning Control for a Class of Time-Delay Systems with Time-Varying Uncertainties." Applied Mechanics and Materials 310 (February 2013): 428–34. http://dx.doi.org/10.4028/www.scientific.net/amm.310.428.

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An adaptive iterative learning control algorithm is proposed for a class of known time-delay nonlinear system with unknown time-varying parameter. A parameter separation technique is used to deal with time-delay problem. The control approach presented in this paper can guarantee that the tracking error converges to zero uniformly on the iteration interval as the iteration number approaches to infinity. A simulation example is provided to illustrate the efficiency of the proposed control algorithm.

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36

Liu, Song, Lei Peng, and Lin Lin Yuan. "Research on Deformation Analysis of Time-Varying Point Cloud." Applied Mechanics and Materials 556-562 (May 2014): 3450–55. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3450.

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For large-scale object or scene which needs high requirements of deformation detection, a comprehensive deformation analysis method is proposed based on the time-varying point cloud to perform continuous detection, to comprehensively analyze the deformation and to research its characteristics and rules. In order to improve computing efficiency, a BSP parallel algorithm based on deformation analysis of time-varying point cloud is designed according to BSP parallel computing technology, and the deformational data are handled by a HAMA computing cluster which is composed of common personal computers. Several computing results from both simulations and real cases have proved the feasibility and effectiveness of analyzing method and BSP analyzing algorithm of deformation of time-varying point cloud.

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37

Robb, David J., and Edward A. Silver. "Probability Density Functions of Task-Processing Times for Deterministic, Time-Varying Processor Efficiency." Journal of the Operational Research Society 41, no. 11 (November 1990): 1049. http://dx.doi.org/10.2307/2582900.

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38

Lin, Boqiang, and Chongchong Zhang. "The overall and time-varying efficiency test for the carbon market in China." Journal of Environmental Management 322 (November 2022): 116072. http://dx.doi.org/10.1016/j.jenvman.2022.116072.

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39

Li, Yuzhou, Min Sheng, Yan Shi, Xiao Ma, and Wanguo Jiao. "Energy Efficiency and Delay Tradeoff for Time-Varying and Interference-Free Wireless Networks." IEEE Transactions on Wireless Communications 13, no. 11 (November 2014): 5921–31. http://dx.doi.org/10.1109/twc.2014.2355206.

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40

Zhibin, Liu, and Liu Haohan. "Multi-factor prediction of water flooding efficiency based on a time-varying system." Communications in Statistics - Theory and Methods 45, no. 20 (January 6, 2016): 5873–83. http://dx.doi.org/10.1080/03610926.2014.950754.

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Robb, David J., and Edward A. Silver. "Probability Density Functions of Task-processing Times for Deterministic, Time-varying Processor Efficiency." Journal of the Operational Research Society 41, no. 11 (November 1990): 1049–52. http://dx.doi.org/10.1057/jors.1990.163.

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42

Kavussanos, Manolis G., and Nikos K. Nomikos. "Constant vs. time-varying hedge ratios and hedging efficiency in the BIFFEX market." Transportation Research Part E: Logistics and Transportation Review 36, no. 4 (December 2000): 229–48. http://dx.doi.org/10.1016/s1366-5545(99)00029-0.

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43

Uddin, Gazi Salah, Jose Areola Hernandez, Syed Jawad Hussain Shahzad, and Seong-Min Yoon. "Time-varying evidence of efficiency, decoupling, and diversification of conventional and Islamic stocks." International Review of Financial Analysis 56 (March 2018): 167–80. http://dx.doi.org/10.1016/j.irfa.2018.01.008.

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44

Al‐Zoubi, Haitham. "Market efficiency, time‐varying volatility and the asymmetric effect in Amman stock exchange." Managerial Finance 33, no. 7 (June 12, 2007): 490–99. http://dx.doi.org/10.1108/03074350710753762.

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45

Khursheed, Ambreen, Muhammad Naeem, Sheraz Ahmed, Faisal Mustafa, and David McMillan. "Adaptive market hypothesis: An empirical analysis of time –varying market efficiency of cryptocurrencies." Cogent Economics & Finance 8, no. 1 (January 1, 2020): 1719574. http://dx.doi.org/10.1080/23322039.2020.1719574.

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46

Kuruppuarachchi, Duminda, Hai Lin, and I. M. Premachandra. "Testing commodity futures market efficiency under time-varying risk premiums and heteroscedastic prices." Economic Modelling 77 (March 2019): 92–112. http://dx.doi.org/10.1016/j.econmod.2017.12.005.

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47

Assaf, A. George, and Alexander Josiassen. "Time-varying production efficiency in the health care foodservice industry: A Bayesian method." Journal of Business Research 65, no. 5 (May 2012): 617–25. http://dx.doi.org/10.1016/j.jbusres.2011.02.049.

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48

Shi, Huai-Long, Zhi-Qiang Jiang, and Wei-Xing Zhou. "Time-Varying Return Predictability in the Chinese Stock Market." Reports in Advances of Physical Sciences 01, no. 01 (March 2017): 1740002. http://dx.doi.org/10.1142/s2424942417400023.

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China’s stock market is the largest emerging market in the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependencies. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic variance ratio test and the generalized spectral test. We find that the return predictability vary over time and a significant return predictability is observed around market turmoils. Our findings are consistent with the Adaptive Markets Hypothesis (AMH) and have practical implications for market participants and policy makers. A predictability index can be constructed for each asset, which might help warn a crisis is in store, ease the development of the ongoing bubble, and stabilize the market.

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49

Stübinger, Johannes, and Dominik Walter. "Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships." Sensors 22, no. 18 (September 12, 2022): 6884. http://dx.doi.org/10.3390/s22186884.

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This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.

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Chen, En Wei, Yi Min Lu, Zheng Shi Liu, and Yong Wang. "Parameter Identification of Time Varying System Using Basic Function Expansion." Advanced Materials Research 403-408 (November 2011): 2800–2804. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2800.

Full text

Abstract:

Time-varying parameters identification in linear system is considered, which can be changed into time-invariant coefficient polynomials after Taylor expansion. Using response data to establish the time-varying autoregressive moving average (TV-ARMA) model, then utilizing least-square algorithm to obtain time-invariant coefficients of time-varying parameters. According to error analysis, to reduce errors and improve accuracy, the estimation time is divided into small internals and the above method is used in each interval. Simulation shows that, under certain error condition, the time-varying parameters obtained by the method have good agreement with the theoretical values; the measures taken have strong anti-interference and high efficiency.

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