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Journal articles on the topic 'Electrical engineering-time series analysis'

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Author: Grafiati

Published: 6 September 2023

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1

Broersen, P. M. T., and S. de Waele. "Time series analysis in a frequency subband." IEEE Transactions on Instrumentation and Measurement 52, no. 4 (August 2003): 1054–60. http://dx.doi.org/10.1109/tim.2003.814823.

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2

OSTERHAGE, HANNES, and KLAUS LEHNERTZ. "NONLINEAR TIME SERIES ANALYSIS IN EPILEPSY." International Journal of Bifurcation and Chaos 17, no. 10 (October 2007): 3305–23. http://dx.doi.org/10.1142/s0218127407019081.

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The framework of the theory of nonlinear dynamics provides powerful concepts and algorithms to study complicated dynamics such as brain electrical activity (electroencephalogram, EEG). Although different influencing factors render the use of nonlinear measures in a strict sense problematic, converging evidence from various investigations now indicates that nonlinear EEG analysis provides a means to reliably characterize different states of physiological and pathophysiological brain function. We here focus on applications of nonlinear EEG analysis in epileptology. Epilepsy affects more than 50 million individuals worldwide – approximately 1% of the world's population. The disease is characterized by a recurrent and sudden malfunction of the brain that is termed seizure. Nonlinear EEG analysis techniques allow to reliably identify the seizure generating structure (epileptic focus) in different areas of the brain even during seizure-free intervals, to disentangle complex spatio-temporal interactions between the epileptic focus and other areas of the brain, and to define a specific state predictive of an impending seizure. Nonlinear EEG analysis provides supplementary information about the epileptogenic process in humans, contributes to an improvement of the presurgical evaluation of epilepsy patients, and offers a basis for the development of new therapy concepts for seizure prevention.

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3

Petitjean, Francois, and Jonathan Weber. "Efficient Satellite Image Time Series Analysis Under Time Warping." IEEE Geoscience and Remote Sensing Letters 11, no. 6 (June 2014): 1143–47. http://dx.doi.org/10.1109/lgrs.2013.2288358.

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4

Stoffer, David S. "Time series analysis by state space models." Automatica 39, no. 5 (May 2003): 954–55. http://dx.doi.org/10.1016/s0005-1098(03)00027-x.

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5

Chen, Ying, and Xiang Jie Chen. "Analysis for TCSC Steady-State Characteristics." Advanced Materials Research 179-180 (January 2011): 1435–40. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.1435.

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When the TCSC steady-state operation, the thyristor turn-on and turn-off time is definite, the changing for TCSC electric capacity voltage and thyristor electric current is with the periodicity and symmetry.Thyristor controlled series compensation technology is fixed series compensation technology foundation, which is meet the needs for adaptation electrical power system operation control developing. With changes the triggering angle for thyristor suitably, then can realize the TCSC equivalent reactance fast, continuously and adjusts smoothly, provides the controllable series compensation for the system, as to achieve increases the system transmitting capacity, enhance the transition condition stability, the damping power oscillation, and the purpose for improvement system tidal current distribution. Although in the entire time axis, obtains the analytic expression for TCSC running status variable is difficulty, but as long as had determined the analytic expression for various electrical quantity in a power frequency cycle, according to the stable state movement's symmetry and periodicity, we can determine the steady state profile that in the entire time axis, and then analyses the TCSC electric circuit’s steady-state characteristic with the time domain computation method. In this paper, topological analysis for TCSC operation established by formula, and then carries on the time domain partition to the TCSC electric circuit solution, finally obtains the steady state fundamental frequency impedance model for TCSC. This paper steady-state characteristic analysis is mainly carries on the topological analysis method to the TCSC main circuit, then establishes the stable state base frequency impedance model for TCSC, and analyses the resonance question for TCSC simultaneously. Then studies TCSC the steady- state characteristic, and with modeling and simulation on them to do further research and analysis, and utilizes the solution method for transformation territory, namely applies the Laplace transform solution equation of state. Thus can be obtained the zero-input response and zero status response formula for system.

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6

Broersen, P. M. T., and R. Bos. "Time-Series Analysis if Data Are Randomly Missing." IEEE Transactions on Instrumentation and Measurement 55, no. 1 (February 2006): 79–84. http://dx.doi.org/10.1109/tim.2005.861247.

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7

Voloshko, Anatolii V., Yaroslav S. Bederak, and Oleksandr A. Kozlovskyi. "AN IMPROVED PRE-FORECASTING ANALYSIS OF ELECTRICAL LOADS OF PUMPING STATION." Resource-Efficient Technologies, no. 4 (February 21, 2020): 20–29. http://dx.doi.org/10.18799/24056537/2019/4/265.

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Relevance of research. In order to reduce energy losses, an accurate and timely forecast of the amount of consumed electricity is necessary. Accurate forecasting of electrical loads of industrial enterprises and their divisions (productions, workshops, departments etc.) allows planning of normal operating conditions, concluding contracts for the electricity supply with the electricity supply company under more favorable conditions, and improving the electricity quality, which ultimately affects the final cost of the products produced by an enterprise. So far, more than 150 forecasting methods of electrical loads have been developed. Usually, the most convenient one is selected based on the forecaster experience by creating and analyzing several forecasting models in order to identify the best. Therefore, in order to simplify the forecasting procedure, it is necessary to develop the methodology for forecasting analysis. This methodology should enable canceling forecasting algorithms that will create lower quality forecasts. The main objective is to develop the methodology for making a forecasting analysis of power consumption on the example of a pumping station of an enterprise with a continuous cycle of work to increase the efficiency of energy consumption and implementation of energy-saving measures. Objects of research: the process of forecasting electrical loads of a pumping station of the enterprise with a continuous cycle of work. Methods of research: fundamental principles of the theory of electrical engineering, statistical methods for power consumption forecasting, the method for detecting the trend of radio signals, and fractal analysis of time series. Research results. The methodology for forecasting analysis of power consumption, which makes it possible to apply the most appropriate methods to forecast the operational power consumption, is developed. For the first time, the radio signal trend detection method is applied to identify the trend of electrical loads. The variation ranges of the fractal parameters of time series of electrical loads are established depending on the variation coefficient of the time series for different periods of time. The Brown method of exponential smoothing that is used to forecast the electrical loads, in the case of identifying the smoothing constant α is in the beyond set ( ), is further improved. The regularity of changes in the fractal parameters of time series of power consumption of a pumping station with an increase in the time series duration and their field of application are explained.

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8

Voloshko, A. V., Ya S. Bederak, and O. A. Kozlovskyi. "AN IMPROVED PRE-FORECASTING ANALYSIS OF ELECTRICAL LOADS OF PUMPING STATION." Resource-Efficient Technologies, no. 4 (February 21, 2020): 20–29. http://dx.doi.org/10.18799/24056529/2019/4/265.

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Relevance of research. In order to reduce energy losses, an accurate and timely forecast of the amount of consumed electricity is necessary. Accurate forecasting of electrical loads of industrial enterprises and their divisions (productions, workshops, departments etc.) allows planning of normal operating conditions, concluding contracts for the electricity supply with the electricity supply company under more favorable conditions, and improving the electricity quality, which ultimately affects the final cost of the products produced by an enterprise. So far, more than 150 forecasting methods of electrical loads have been developed. Usually, the most convenient one is selected based on the forecaster experience by creating and analyzing several forecasting models in order to identify the best. Therefore, in order to simplify the forecasting procedure, it is necessary to develop the methodology for forecasting analysis. This methodology should enable canceling forecasting algorithms that will create lower quality forecasts. The main objective is to develop the methodology for making a forecasting analysis of power consumption on the example of a pumping station of an enterprise with a continuous cycle of work to increase the efficiency of energy consumption and implementation of energy-saving measures. Objects of research: the process of forecasting electrical loads of a pumping station of the enterprise with a continuous cycle of work. Methods of research: fundamental principles of the theory of electrical engineering, statistical methods for power consumption forecasting, the method for detecting the trend of radio signals, and fractal analysis of time series. Research results. The methodology for forecasting analysis of power consumption, which makes it possible to apply the most appropriate methods to forecast the operational power consumption, is developed. For the first time, the radio signal trend detection method is applied to identify the trend of electrical loads. The variation ranges of the fractal parameters of time series of electrical loads are established depending on the variation coefficient of the time series for different periods of time. The Brown method of exponential smoothing that is used to forecast the electrical loads, in the case of identifying the smoothing constant α is in the beyond set ( ), is further improved. The regularity of changes in the fractal parameters of time series of power consumption of a pumping station with an increase in the time series duration and their field of application are explained.

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9

Vázquez, J., E. Olías, A. Barrado, and J. Pleite. "Analysis of long time series of environmental electromagnetic field." Electronics Letters 39, no. 1 (2003): 125. http://dx.doi.org/10.1049/el:20030052.

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10

Wang, Jiang, Li Sun, Xiangyang Fei, and Bing Zhu. "Chaos analysis of the electrical signal time series evoked by acupuncture." Chaos, Solitons & Fractals 33, no. 3 (August 2007): 901–7. http://dx.doi.org/10.1016/j.chaos.2006.01.040.

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11

Cuomo, V., G. Di Bello, V. Lapenna, M. Macchiato, and C. Serio. "Parametric time series analysis of extreme events in earthquake electrical precursors." Tectonophysics 262, no. 1-4 (September 1996): 159–72. http://dx.doi.org/10.1016/0040-1951(95)00212-x.

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12

Luo, Yu, and Yulin Wang. "A Statistical Time-Frequency Model for Non-stationary Time Series Analysis." IEEE Transactions on Signal Processing 68 (2020): 4757–72. http://dx.doi.org/10.1109/tsp.2020.3014607.

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13

Sinha, N. K. "Recursive estimation and time-series analysis: An introduction." Automatica 22, no. 3 (May 1986): 388. http://dx.doi.org/10.1016/0005-1098(86)90042-7.

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14

Ozawa, Kazuhiro, ’Takahide Niimura, and Tomoaki Nakashima. "Fuzzy Time-Series Model of Electric Power Consumption." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 3 (May 20, 2000): 188–94. http://dx.doi.org/10.20965/jaciii.2000.p0188.

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In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Tiraditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely stochastic models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy autoregression, and apply it to the analysis of time-series data of electric power consumption. Two models, which are different in complexity, are presented, and the performance of the models are evaluated by vagueness and α-cuts. The proposed fuzzy Auoregression model represents the rich information of uncertainty that the original data contain, and it can be a powerful tool for flexible decision-making with uncertainty. The fuzzy AR model can also be constructed in relatively simple procedure compared with the conventional approaches.

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15

Cong, L., G. Lu, Y. Chen, B. Deng, and X. Li. "Network congestion estimation using packet time series analysis." IET Communications 4, no. 8 (2010): 980. http://dx.doi.org/10.1049/iet-com.2009.0506.

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16

Abbasi, Ali Reza, Mohammad Reza Mahmoudi, and Mohammad Mehdi Arefi. "Transformer Winding Faults Detection Based on Time Series Analysis." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–10. http://dx.doi.org/10.1109/tim.2021.3076835.

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17

Malhotra, Akshay, Ioannis D. Schizas, and Vangelis Metsis. "Correlation Analysis-Based Classification of Human Activity Time Series." IEEE Sensors Journal 18, no. 19 (October 1, 2018): 8085–95. http://dx.doi.org/10.1109/jsen.2018.2864207.

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18

Nayak, Nikhil, and Rujula Singh R. "5G Traffic Prediction with Time Series Analysis." International Journal of Innovative Technology and Exploring Engineering 10, no. 12 (October 30, 2021): 36–40. http://dx.doi.org/10.35940/ijitee.l9555.10101221.

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In today’s day and age, a mobile phone has become a basic requirement needed for anyone to thrive. With the cellular traffic demand increasing so dramatically, it is now necessary to accurately predict the user traffic in cellular networks, to improve the performance in terms of resource allocation and utilization. Since traffic learning and prediction is a classical and appealing field, which still yields many meaningful results, there has been an increasing interest in leveraging Machine Learning tools to analyze the total traffic served in each region, to optimize the operation of the network. With the help of this project, we seek to exploit the traffic history by using it to predict the nature and occurrence of future traffic. Furthermore, we classify the traffic into application types, to increase our understanding of the nature of the traffic. By leveraging the power of machine learning and identifying its usefulness in the field of cellular networks we try to achieve three main objectives - classification of the application generating the traffic, prediction of packet arrival intensity and burst occurrence. The design of the prediction and classification system is done using Long Short Term Memory (LSTM) model. The LSTM predictor developed in this experiment would return the number of uplink packets and estimate the probability of burst occurrence in the specified future time interval. For the purpose of classification, the regression layer in our LSTM prediction model is replaced by a SoftMax classifier which is used to classify the application generating the cellular traffic into one of the four applications including surfing, video calling, voice calling, and video streaming.

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19

West, Bruce J., Artur Maciejewski, Miroslaw Latka, Tadeusz Sebzda, Zbigniew Swierczynski, Sylwia Cybulska-Okołow, and Eugeniusz Baran. "Wavelet analysis of scaling properties of gastric electrical activity." Journal of Applied Physiology 101, no. 5 (November 2006): 1425–31. http://dx.doi.org/10.1152/japplphysiol.01364.2004.

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We present a novel approach to the analysis of fluctuations in human myoelectrical gastric activity measured noninvasively from the surface of the abdomen. The time intervals between successive maxima of the wavelet transformed quasi-periodic electrogastrographic waveform define the gastric rate variability (GRV) time series. By using the method of average wavelet coefficients, the statistical fluctuations in the GRV signal in healthy individuals are determined to scale in time. Such scaling was previously found in a variety of physiological phenomena, all of which support the hypothesis that physiological dynamics utilize fractal time series. We determine the scaling index in a cohort of 17 healthy individuals to be 0.80 ± 0.14, which compared with a set of surrogate data is found to be significant at the level P < 0.01. We also determined that the dynamical pattern, so evident in the spectrum of average wavelet coefficients of the GRV time series of healthy individuals, is significantly reduced in a cohort of systemic sclerosis patients having a scaling index 0.64 ± 0.17. These results imply that the long-term memory in GRV time series is significantly reduced from healthy individuals to those with systemic sclerosis. Consequently, this disease degrades the complexity of the underlying gastrointestinal control system and this degradation is manifest in the loss of scaling in the GRV time series.

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20

Yan, Ruqiang, Yongbin Liu, and Robert Gao. "Correlation Dimension Analysis: A Non-linear Time Series Analysis for Data Processing." IEEE Instrumentation & Measurement Magazine 13, no. 6 (December 2010): 19–25. http://dx.doi.org/10.1109/mim.2010.5669609.

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21

Thomson, David J., and Frank L. Vernon. "Some Comments on the Analysis of “Big” Scientific Time Series." Proceedings of the IEEE 104, no. 11 (November 2016): 2220–49. http://dx.doi.org/10.1109/jproc.2016.2598218.

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22

Gorlov, M. I., A. V. Strogonov, and D. Yu Smirnov. "Transistor-degradation prediction by time-series analysis." Russian Microelectronics 35, no. 5 (September 2006): 337–44. http://dx.doi.org/10.1134/s106373970605009x.

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23

Shi, Ying, Tao Yu, Qianjin Liu, Hanxin Zhu, Fusheng Li, and Yaxiong Wu. "An Approach of Electrical Load Profile Analysis Based on Time Series Data Mining." IEEE Access 8 (2020): 209915–25. http://dx.doi.org/10.1109/access.2020.3019698.

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24

Elger, Christian E., and Klaus Lehnertz. "Seizure prediction by non‐linear time series analysis of brain electrical activity." European Journal of Neuroscience 10, no. 2 (February 1998): 786–89. http://dx.doi.org/10.1046/j.1460-9568.1998.00090.x.

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25

Karpenko, S. M., N. V. Karpenko, and G. Y. Bezginov. "Forecasting of power consumption at mining enterprises using statistical methods." Mining Industry Journal (Gornay Promishlennost), no. 1/2022 (March 15, 2022): 82–88. http://dx.doi.org/10.30686/1609-9192-2022-1-82-88.

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Forecasting of electric power consumption with due account of assessed impact of various factors helps to make efficient technical and managerial decisions to optimize the electric power consumption processes, including preparation of bids for the wholesale electric power and capacity market. The article uses multivariate methods of statistical analysis and econometric methods based on time series analysis for model designing. The paper presents the results of developing the following models: a multifactor model of electrical power consumption using the regression analysis, the Principal Component Method with the assessment of the impact of production factors on electrical power consumption using elasticity coefficients, as well as the energy saving factor based on a variable structure model; trend additive and multiplicative forecast models of electrical consumption that take into account the seasonality factor, models with a change in trends, a linear dynamic model of electrical power consumption that takes into account the production output; a forecast adaptive polynomial model of electrical power consumption as well as the Winters model. The developed forecast models have a sufficiently high accuracy (accuracy of the MAPE was below 7%). The choice of the model type to forecast the electrical power consumption depends on the quantitative and qualitative characteristics of the time series, the structural relation between the series, the purpose and objectives of the modeling. In order to enhance the accuracy of the forecast it is required to regularly refine the model and adjust it to the actual situation with the due account of new factors and production trends while building different versions of scenarios and combined forecast models of electrical power consumption

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26

Barzola-Monteses, Julio, Mónica Mite-León, Mayken Espinoza-Andaluz, Juan Gómez-Romero, and Waldo Fajardo. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case." Sustainability 11, no. 23 (November 20, 2019): 6539. http://dx.doi.org/10.3390/su11236539.

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Electrical generation in Ecuador mainly comes from hydroelectric and thermo-fossil sources, with the former amounting to almost half of the national production. Even though hydroelectric power sources are highly stable, there is a threat of droughts and floods affecting Ecuadorian water reservoirs and producing electrical faults, as highlighted by the 2009 Ecuador electricity crisis. Therefore, predicting the behavior of the hydroelectric system is crucial to develop appropriate planning strategies and a good starting point for energy policy decisions. In this paper, we developed a time series predictive model of hydroelectric power production in Ecuador. To this aim, we used production and precipitation data from 2000 to 2015 and compared the Box-Jenkins (ARIMA) and the Box-Tiao (ARIMAX) regression methods. The results showed that the best model is the ARIMAX (1,1,1) (1,0,0)12, which considers an exogenous variable precipitation in the Napo River basin and can accurately predict monthly production values up to a year in advance. This model can provide valuable insights to Ecuadorian energy managers and policymakers.

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27

Liu, Xu, S. Islam, A. A. Chowdhury, and D. O. Koval. "Tired of Continuous Time-Series Analysis or Calculations?" IEEE Industry Applications Magazine 16, no. 5 (September 2010): 59–65. http://dx.doi.org/10.1109/mias.2010.937447.

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28

Korenberg, M. J., and L. D. Paarmann. "Orthogonal approaches to time-series analysis and system identification." IEEE Signal Processing Magazine 8, no. 3 (July 1991): 29–43. http://dx.doi.org/10.1109/79.127999.

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29

Kobayashi, Shin-ichi, Yasuyuki Shirai, Kazuo Hiyane, Fumihiro Kumeno, Hiroshi Inujima, and Noriyoshi Yamauchi. "Time Series Analysis of Technology Trends based on the Internet Resources." IEEJ Transactions on Electronics, Information and Systems 125, no. 5 (2005): 720–29. http://dx.doi.org/10.1541/ieejeiss.125.720.

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30

Erdoğan, Hediye, and Niyazi Arslan. "Identification of vertical total electron content by time series analysis." Digital Signal Processing 19, no. 4 (July 2009): 740–49. http://dx.doi.org/10.1016/j.dsp.2008.07.002.

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31

Alonso-Gonzalez, Alberto, Carlos Lopez-Martinez, Konstantinos P. Papathanassiou, and Irena Hajnsek. "Polarimetric SAR Time Series Change Analysis Over Agricultural Areas." IEEE Transactions on Geoscience and Remote Sensing 58, no. 10 (October 2020): 7317–30. http://dx.doi.org/10.1109/tgrs.2020.2981929.

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32

Aggarwal, Akarsh, Mohammed Alshehri, Manoj Kumar, Osama Alfarraj, Purushottam Sharma, and Kamal Raj Pardasani. "Landslide data analysis using various time-series forecasting models." Computers & Electrical Engineering 88 (December 2020): 106858. http://dx.doi.org/10.1016/j.compeleceng.2020.106858.

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33

Serrano-Guerrero, Xavier, Guillermo Escrivá-Escrivá, Santiago Luna-Romero, and Jean-Michel Clairand. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles." Energies 13, no. 5 (February 26, 2020): 1046. http://dx.doi.org/10.3390/en13051046.

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Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.

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34

Faridi, Muhammad Zahir, Hina Ali, Tahira Bano Qasim, and Maheen Sadaf. "Electrical Consumption in the Industrial Sector of Pakistan: A Structural Time Series Analysis." Review of Economics and Development Studies 8, no. 2 (June 30, 2022): 203–9. http://dx.doi.org/10.47067/reads.v8i2.453.

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The usage of energy is the central factor in promoting the contribution of the industrial sector economy to develop the economy. Indeed, Pakistan is a growing but recently united country with severe shortages of electricity resources. Therefore, the industry has a growing quantity of growth that leads to economic progress and also prioritizes inevitable challenges to stimulate industrial growth. The purpose of the study is to investigate the relationship between electrical consumption, oil consumption, and gas usage in manufacturing and its impact on the economic growth in Pakistan. Results declared electricity and gas have a positive and significant impact in the short and long run while fuel consumption has a negative shock on the economy in the short run but is positive in long run. The fault correction model (VECM) ensures bilateral relations in the industrialized sector, oil consumption and financial progress in Pakistan. In addition, findings revealed a strong correlation between the variables tested and suggested that the Pakistani government must develop a strong policy to reduce gas and oil to produce electricity, instead of relying on solar energy, water, airstream and biomass sources. Therefore, the government should increase stokes and storage of oil and gas to provide the industrial sector at a cheap rate.

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35

Chen, Xiangrong, Yang Xu, and Xiaolong Cao. "Nonlinear time series analysis of partial discharges in electrical trees of XLPE cable insulation samples." IEEE Transactions on Dielectrics and Electrical Insulation 21, no. 4 (August 2014): 1455–61. http://dx.doi.org/10.1109/tdei.2014.004307.

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36

Nickerson, Paul V., Raheleh Baharloo, Amal A. Wanigatunga, Todd M. Manini, Patrick J. Tighe, and Parisa Rashidi. "Transition Icons for Time-Series Visualization and Exploratory Analysis." IEEE Journal of Biomedical and Health Informatics 22, no. 2 (March 2018): 623–30. http://dx.doi.org/10.1109/jbhi.2017.2704608.

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37

Petrov, L., P. Lewin, and Tadeusz Czaszejko. "On the applicability of nonlinear time series methods for partial discharge analysis." IEEE Transactions on Dielectrics and Electrical Insulation 21, no. 1 (February 2014): 284–93. http://dx.doi.org/10.1109/tdei.2014.6740751.

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38

Feng, Jing. "Analysis of Driving Factors of Innovation and Entrepreneurship Based on Time Series Analysis." Journal of Sensors 2021 (October 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/8427336.

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In order to analyze the driving factors of innovation and entrepreneurship, based on the time series analysis algorithm, this paper combines the analysis requirements of innovation and entrepreneurship driving factors to improve the time series, uses decomposition methods to decompose the complex original data into relatively simple components and reconstruct them, and predicts the reconstructed components to integrate the final predicted value. Moreover, this paper introduces entrepreneurial attitude as an intermediary variable and verifies it through data collection and statistical analysis, so that entrepreneurial traits influence entrepreneurial propensity through entrepreneurial attitude. The test results show that entrepreneurial attitude can better explain the influence of entrepreneurial traits on entrepreneurial propensity. In addition, this paper constructs an analysis model of driving factors for innovation and entrepreneurship, obtains experimental data through questionnaire survey methods, and conducts experimental research in combination with mathematical statistics. From the statistical results, it can be seen that the innovative and entrepreneurial driving factor analysis model based on time series analysis proposed in this paper is effective.

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39

Poskitt, D. S., and Shin-Ho Chung. "Markov chain models, time series analysis and extreme value theory." Advances in Applied Probability 28, no. 2 (June 1996): 405–25. http://dx.doi.org/10.2307/1428065.

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Markov chain processes are becoming increasingly popular as a means of modelling various phenomena in different disciplines. For example, a new approach to the investigation of the electrical activity of molecular structures known as ion channels is to analyse raw digitized current recordings using Markov chain models. An outstanding question which arises with the application of such models is how to determine the number of states required for the Markov chain to characterize the observed process. In this paper we derive a realization theorem showing that observations on a finite state Markov chain embedded in continuous noise can be synthesized as values obtained from an autoregressive moving-average data generating mechanism. We then use this realization result to motivate the construction of a procedure for identifying the state dimension of the hidden Markov chain. The identification technique is based on a new approach to the estimation of the order of an autoregressive moving-average process. Conditions for the method to produce strongly consistent estimates of the state dimension are given. The asymptotic distribution of the statistic underlying the identification process is also presented and shown to yield critical values commensurate with the requirements for strong consistency.

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40

Poskitt, D. S., and Shin-Ho Chung. "Markov chain models, time series analysis and extreme value theory." Advances in Applied Probability 28, no. 02 (June 1996): 405–25. http://dx.doi.org/10.1017/s0001867800048552.

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Markov chain processes are becoming increasingly popular as a means of modelling various phenomena in different disciplines. For example, a new approach to the investigation of the electrical activity of molecular structures known as ion channels is to analyse raw digitized current recordings using Markov chain models. An outstanding question which arises with the application of such models is how to determine the number of states required for the Markov chain to characterize the observed process. In this paper we derive a realization theorem showing that observations on a finite state Markov chain embedded in continuous noise can be synthesized as values obtained from an autoregressive moving-average data generating mechanism. We then use this realization result to motivate the construction of a procedure for identifying the state dimension of the hidden Markov chain. The identification technique is based on a new approach to the estimation of the order of an autoregressive moving-average process. Conditions for the method to produce strongly consistent estimates of the state dimension are given. The asymptotic distribution of the statistic underlying the identification process is also presented and shown to yield critical values commensurate with the requirements for strong consistency.

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Gülal, Engin, Hediye Erdoğan, and İbrahim Tiryakioğlu. "Research on the stability analysis of GNSS reference stations network by time series analysis." Digital Signal Processing 23, no. 6 (December 2013): 1945–57. http://dx.doi.org/10.1016/j.dsp.2013.06.014.

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Whitcher, Brandon, Peter F. Craigmile, and Peter Brown. "Time-varying spectral analysis in neurophysiological time series using Hilbert wavelet pairs." Signal Processing 85, no. 11 (November 2005): 2065–81. http://dx.doi.org/10.1016/j.sigpro.2005.07.002.

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Lin, Bor-Ren, and Chien-Lan Huang. "Analysis of series resonant converter with series–parallel connection." International Journal of Electronics 98, no. 2 (February 2011): 249–62. http://dx.doi.org/10.1080/00207217.2010.520150.

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Fedjajevs, Andrejs, Willemijn Groenendaal, Carlos Agell, and Evelien Hermeling. "Platform for Analysis and Labeling of Medical Time Series." Sensors 20, no. 24 (December 19, 2020): 7302. http://dx.doi.org/10.3390/s20247302.

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Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.

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Priyadarshini, Ishaani, Ahmed Alkhayyat, Anita Gehlot, and Raghvendra Kumar. "Time series analysis and anomaly detection for trustworthy smart homes." Computers and Electrical Engineering 102 (September 2022): 108193. http://dx.doi.org/10.1016/j.compeleceng.2022.108193.

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Drasko Furundzic. "Application example of neural networks for time series analysis:." Signal Processing 64, no. 3 (February 1998): 383–96. http://dx.doi.org/10.1016/s0165-1684(97)00203-x.

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Ambrogioni, Luca, and Eric Maris. "Complex-valued gaussian process regression for time series analysis." Signal Processing 160 (July 2019): 215–28. http://dx.doi.org/10.1016/j.sigpro.2019.02.011.

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Rubio-León, José, José Rubio-Cienfuegos, Cristian Vidal-Silva, Jesennia Cárdenas-Cobo, and Vannessa Duarte. "Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models." Mathematics 11, no. 17 (August 25, 2023): 3667. http://dx.doi.org/10.3390/math11173667.

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Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

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Omenzetter, Piotr, and James Mark William Brownjohn. "Application of time series analysis for bridge monitoring." Smart Materials and Structures 15, no. 1 (January 9, 2006): 129–38. http://dx.doi.org/10.1088/0964-1726/15/1/041.

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Ding, Xue Cheng, Zheng You He, and Min Yu. "Traction Substation Electrical Main Connection Reliability Analysis." Advanced Materials Research 433-440 (January 2012): 7293–99. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.7293.

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Traction substation reliability is of vital importance for railway transportation safety. To illustrate traction substation reliability, irreparable and reparable reliability models of three types of traction substation Electrical main connection have been established. Based on analysis of simple series and parallel reliability system, system irreparable reliability model is analyzed. The ways of how to get mean time to failure (MTTF) and mean time to first failure (MTTFF) of reparable system are achieved. By comparative analysis of the value of MTTF and MTTFF among three kinds of traction substation main connection reparable and irreparable system, some useful conclusions are found.

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