Добірка наукової літератури з теми "Electrical engineering-time series analysis"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Electrical engineering-time series analysis".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Electrical engineering-time series analysis"
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.
Повний текст джерелаOSTERHAGE, 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.
Повний текст джерелаPetitjean, 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.
Повний текст джерелаStoffer, 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.
Повний текст джерелаChen, 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.
Повний текст джерелаBroersen, 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.
Повний текст джерелаVoloshko, 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.
Повний текст джерелаVoloshko, 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.
Повний текст джерелаVázquez, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаДисертації з теми "Electrical engineering-time series analysis"
Rawizza, Mark Alan. "Time-series analysis of multivariate manufacturing data sets." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10895.
Повний текст джерелаSingh, Pushpendra. "Some studies on a generalized fourier expansion for nonlinear and nonstationary time series analysis." Thesis, IIT Delhi, 2016. http://localhost:8080/xmlui/handle/12345678/7064.
Повний текст джерелаLee, Fung-Man. "Studies in time series analysis and forecasting of energy data." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36032.
Повний текст джерелаHutchinson, James M. "A radial basis function approach to financial time series analysis." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/12216.
Повний текст джерелаIncludes bibliographical references (p. 153-159).
by James M. Hutchinson.
Ph.D.
Modlin, Danny Robert. "Utilizing time series analysis to forecast long-term electrical consumption /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/modlind/dannymodlin.pdf.
Повний текст джерелаDzunic, Zoran Ph D. Massachusetts Institute of Technology. "A Bayesian latent time-series model for switching temporal interaction analysis." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103723.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 153-157).
We introduce a Bayesian discrete-time framework for switching-interaction analysis under uncertainty, in which latent interactions, switching pattern and signal states and dynamics are inferred from noisy and possibly missing observations of these signals. We propose reasoning over posterior distribution of these latent variables as a means of combating and characterizing uncertainty. This approach also allows for answering a variety of questions probabilistically, which is suitable for exploratory pattern discovery and post-analysis by human experts. This framework is based on a Bayesian learning of the structure of a switching dynamic Bayesian network (DBN) and utilizes a states-pace approach to allow for noisy observations and missing data. It generalizes the autoregressive switching interaction model of Siracusa et al. [50], which does not allow observation noise, and the switching linear dynamic system model of Fox et al. [16], which does not infer interactions among signals. We develop a Gibbs sampling inference procedure, which is particularly efficient in the case of linear Gaussian dynamics and observation models. We use a modular prior over structures and a bound on the number of parent sets per signal to reduce the number of structures to consider from super-exponential to polynomial. We provide a procedure for setting the parameters of the prior and initializing latent variables that leads to a successful application of the inference algorithm in practice, and leaves only few general parameters to be set by the user. A detailed analysis of the computational and memory complexity of each step of the algorithm is also provided. We demonstrate the utility of our framework on different types of data. Different benefits of the proposed approach are illustrated using synthetic data. Most real data do not contain annotation of interactions. To demonstrate the ability of the algorithm to infer interactions and the switching pattern from time-series data in a realistic setting, joystick data is created, which is a controlled, human-generated data that implies ground truth annotations by design. Climate data is a real data used to illustrate the variety of applications and types of analyses enabled by the developed methodology. Finally, we apply the developed model to the problem of structural health monitoring in civil engineering. Time-series data from accelerometers located at multiple positions on a building are obtained for two laboratory model structures and a real building. We analyze the results of interaction analysis and how the inferred dependencies among sensor signals relate to the physical structure and properties of the building, as well as the environment and excitation conditions. We develop time-series classification and single-class classification extensions of the model and apply them to the problem of damage detection. We show that the method distinguishes time-series obtained under different conditions with high accuracy, in both supervised and single-class classification setups.
by Zoran Dzunic.
Ph. D.
Lindberg, Johan. "A Time Series Forecast of the Electrical Spot Price : Time series analysis applied to the Nordic power market." Thesis, Umeå universitet, Institutionen för fysik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-41898.
Повний текст джерелаThungtong, Anurak. "Synchronization, Variability, and Nonlinearity Analysis: Applications to Physiological Time Series." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1364316597.
Повний текст джерелаShashidhar, Akhil. "Generalized Volterra-Wiener and surrogate data methods for complex time series analysis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/41619.
Повний текст джерелаIncludes bibliographical references (leaves 133-150).
This thesis describes the current state-of-the-art in nonlinear time series analysis, bringing together approaches from a broad range of disciplines including the non-linear dynamical systems, nonlinear modeling theory, time-series hypothesis testing, information theory, and self-similarity. We stress mathematical and qualitative relationships between key algorithms in the respective disciplines in addition to describing new robust approaches to solving classically intractable problems. Part I presents a comprehensive review of various classical approaches to time series analysis from both deterministic and stochastic points of view. We focus on using these classical methods for quantification of complexity in addition to proposing a unified approach to complexity quantification encapsulating several previous approaches. Part II presents robust modern tools for time series analysis including surrogate data and Volterra-Wiener modeling. We describe new algorithms converging the two approaches that provide both a sensitive test for nonlinear dynamics and a noise-robust metric for chaos intensity.
by Akhil Shashidhar.
M.Eng.
Siracusa, Michael Richard 1980. "Dynamic dependence analysis : modeling and inference of changing dependence among multiple time-series." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53303.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (p. 183-190).
In this dissertation we investigate the problem of reasoning over evolving structures which describe the dependence among multiple, possibly vector-valued, time-series. Such problems arise naturally in variety of settings. Consider the problem of object interaction analysis. Given tracks of multiple moving objects one may wish to describe if and how these objects are interacting over time. Alternatively, consider a scenario in which one observes multiple video streams representing participants in a conversation. Given a single audio stream, one may wish to determine with which video stream the audio stream is associated as a means of indicating who is speaking at any point in time. Both of these problems can be cast as inference over dependence structures. In the absence of training data, such reasoning is challenging for several reasons. If one is solely interested in the structure of dependence as described by a graphical model, there is the question of how to account for unknown parameters. Additionally, the set of possible structures is generally super-exponential in the number of time series. Furthermore, if one wishes to reason about structure which varies over time, the number of structural sequences grows exponentially with the length of time being analyzed. We present tractable methods for reasoning in such scenarios. We consider two approaches for reasoning over structure while treating the unknown parameters as nuisance variables. First, we develop a generalized likelihood approach in which point estimates of parameters are used in place of the unknown quantities. We explore this approach in scenarios in which one considers a small enumerated set of specified structures.
(cont.) Second, we develop a Bayesian approach and present a conjugate prior on the parameters and structure of a model describing the dependence among time-series. This allows for Bayesian reasoning over structure while integrating over parameters. The modular nature of the prior we define allows one to reason over a super-exponential number of structures in exponential-time in general. Furthermore, by imposing simple local or global structural constraints we show that one can reduce the exponential-time complexity to polynomial-time complexity while still reasoning over a super-exponential number of candidate structures. We cast the problem of reasoning over temporally evolving structures as inference over a latent state sequence which indexes structure over time in a dynamic Bayesian network. This model allows one to utilize standard algorithms such as Expectation Maximization, Viterbi decoding, forward-backward messaging and Gibbs sampling in order to efficiently reasoning over an exponential number of structural sequences. We demonstrate the utility of our methodology on two tasks: audio-visual association and moving object interaction analysis. We achieve state-of-the-art performance on a standard audio-visual dataset and show how our model allows one to tractably make exact probabilistic statements about interactions among multiple moving objects.
by Michael Richard Siracusa.
Ph.D.
Книги з теми "Electrical engineering-time series analysis"
Mohajer, Maryam. Monlinear time series analysis of electrical activity in a slice model of epilepsy. Ottawa: National Library of Canada, 1999.
Знайти повний текст джерелаWei, Y. Common waveform analysis: A new and practical generalization of Fourier analysis. Boston: Kluwer Academic Publishers, 2000.
Знайти повний текст джерела1927-, Akaike Hirotsugu, and Kitagawa G. 1948-, eds. The practice of time series analysis. New York: Springer, 1999.
Знайти повний текст джерелаReinsel, Gregory C. Elements of Multivariate Time Series Analysis. New York, NY: Springer US, 1993.
Знайти повний текст джерелаIntroduction to Multiple Time Series Analysis. 2nd ed. Berlin: Springer-Verlag, 1993.
Знайти повний текст джерелаIntroduction to multiple time series analysis. Berlin: Springer-Verlag, 1991.
Знайти повний текст джерелаMachiwal, Deepesh. Hydrologic Time Series Analysis: Theory and Practice. Dordrecht: Springer Netherlands, 2012.
Знайти повний текст джерелаBox, George E. P. Time series analysis: Forecasting and control. 4th ed. Hoboken, N.J: John Wiley, 2008.
Знайти повний текст джерелаBox, George E. P. Time series analysis: Forecasting and control. 3rd ed. Englewood Cliffs, N.J: Prentice Hall, 1994.
Знайти повний текст джерелаBox, George E. P. Time series analysis: Forecasting and control. 4th ed. Hoboken, N.J: John Wiley, 2008.
Знайти повний текст джерелаЧастини книг з теми "Electrical engineering-time series analysis"
Ao, Sio-Iong. "Applied Time Series Analysis." In Lecture Notes in Electrical Engineering, 9–24. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8768-3_2.
Повний текст джерелаKathuria, Charu, Deepti Mehrotra, Shalini Bhartiya, and Navnit Kumar Misra. "The Analysis of Time Series Data." In Lecture Notes in Electrical Engineering, 225–35. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4687-5_17.
Повний текст джерелаHe, Yuefan, Shuangcheng Zhang, Qianyi Wang, Qi Liu, Wei Qu, and Xiaowei Hou. "HECTOR for Analysis of GPS Time Series." In Lecture Notes in Electrical Engineering, 187–96. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0005-9_16.
Повний текст джерелаSai Anand, M., and R. Ramalakshmi. "Time Series Analysis to Forecast Wind Speed." In Lecture Notes in Electrical Engineering, 389–402. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2177-3_38.
Повний текст джерелаHahm, Jaegyoon, Oh-Kyoung Kwon, Sangwan Kim, Yong-Hwan Jung, Joon-Weon Yoon, Joo Hyun Kim, Mi-Kyoung Kim, Yong-Ik Byun, Min-Su Shin, and Chanyeol Park. "Astronomical Time Series Data Analysis Leveraging Science Cloud." In Lecture Notes in Electrical Engineering, 493–500. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5076-0_60.
Повний текст джерелаCheng, Xiaorong, and Xiaomeng Zhao. "Network Risk Prediction Based on Time Series Analysis." In Lecture Notes in Electrical Engineering, 713–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40618-8_92.
Повний текст джерелаMa, Yiguo, Guanchen Zhou, and Ying Jiao. "Geographical Profile Based on Time-Series Analysis Model." In Lecture Notes in Electrical Engineering, 35–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40630-0_5.
Повний текст джерелаAhmia, Oussama, and Nadir Farah. "Electrical Load Forecasting: A Parallel Seasonal Approach." In Time Series Analysis and Forecasting, 355–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28725-6_26.
Повний текст джерелаZhao, Zhihong, Shaopu Yang, and Yu Lei. "The Application of Multiwavelets to Chaotic Time Series Analysis." In Lecture Notes in Electrical Engineering, 51–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38460-8_6.
Повний текст джерелаJuneja, Tanya, Shalini Bhaskar Bajaj, and Nishu Sethi. "Synthetic Time Series Data Generation Using Time GAN with Synthetic and Real-Time Data Analysis." In Lecture Notes in Electrical Engineering, 657–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0601-7_51.
Повний текст джерелаТези доповідей конференцій з теми "Electrical engineering-time series analysis"
N, Shilpa G., and G. S. Sheshadri. "Electrical Load Forecasting Using Time Series Analysis." In 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC). IEEE, 2020. http://dx.doi.org/10.1109/b-htc50970.2020.9297986.
Повний текст джерелаLimthong, Kriangkrai, Fukuda Kensuke, and Pirawat Watanapongse. "Wavelet-Based Unwanted Traffic Time Series Analysis." In 2008 International Conference on Computer and Electrical Engineering (ICCEE). IEEE, 2008. http://dx.doi.org/10.1109/iccee.2008.106.
Повний текст джерелаLukhyswara, Pandu, Lesnanto Multa Putranto, and Dyonisius Dony Ariananda. "Solar Irradiation Forecasting Uses Time Series Analysis." In 2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2019. http://dx.doi.org/10.1109/iciteed.2019.8929990.
Повний текст джерелаBalli, Tugce, and Ramaswamy Palaniappan. "EEG time series analysis with exponential autoregressive modelling." In 2008 Canadian Conference on Electrical and Computer Engineering - CCECE. IEEE, 2008. http://dx.doi.org/10.1109/ccece.2008.4564581.
Повний текст джерелаHolzapfel, C. "Time Series Analysis in the Study of Sliding Electrical Contacts." In 2012 IEEE 58th Holm Conference on Electrical Contacts (Holm 2012). IEEE, 2012. http://dx.doi.org/10.1109/holm.2012.6336586.
Повний текст джерелаIntachai, Prakit, and Peerapol Yuvapoositanon. "A Singular Spectrum Analysis Precoded Deep Learning Architecture for Forecasting Currency Time Series." In 2018 International Electrical Engineering Congress (iEECON). IEEE, 2018. http://dx.doi.org/10.1109/ieecon.2018.8712120.
Повний текст джерелаSinitsyna, Kseniia, Anton Petrochenkov, and Bernd Krause. "Some Practical Aspects of Electric Power Consumption Time Series Analysis." In 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). IEEE, 2019. http://dx.doi.org/10.1109/rtucon48111.2019.8982264.
Повний текст джерелаGuarnaccia, C., L. Elia, J. Quartieri, and C. Tepedino. "Time series analysis techniques applied to transportation noise." In 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2017. http://dx.doi.org/10.1109/eeeic.2017.7977739.
Повний текст джерелаMontillet, Jean-Philippe, and Kegen Yu. "Covariance matrix analysis for higher order fractional Brownian motion time series." In 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2015. http://dx.doi.org/10.1109/ccece.2015.7129488.
Повний текст джерелаZhou, Yuguo, and Ziwen Zhang. "Research on Subsidence and Groundwater Based on SBAS Time-Series Analysis." In ICITEE-2019: 2nd International Conference on Information Technologies and Electrical Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3386415.3386954.
Повний текст джерелаЗвіти організацій з теми "Electrical engineering-time series analysis"
Modlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3677.
Повний текст джерелаMorin, Shai, Gregory Walker, Linda Walling, and Asaph Aharoni. Identifying Arabidopsis thaliana Defense Genes to Phloem-feeding Insects. United States Department of Agriculture, February 2013. http://dx.doi.org/10.32747/2013.7699836.bard.
Повний текст джерелаMazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch, and Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/zjdn7385.
Повний текст джерелаTorres, Marissa, Norberto Nadal-Caraballo, and Alexandros Taflanidis. Rapid tidal reconstruction for the Coastal Hazards System and StormSim part II : Puerto Rico and U.S. Virgin Islands. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41482.
Повний текст джерелаVargas-Herrera, Hernando, Juan Jose Ospina-Tejeiro, Carlos Alfonso Huertas-Campos, Adolfo León Cobo-Serna, Edgar Caicedo-García, Juan Pablo Cote-Barón, Nicolás Martínez-Cortés, et al. Monetary Policy Report - April de 2021. Banco de la República de Colombia, July 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr2-2021.
Повний текст джерела