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Rozprawy doktorskie na temat „Electrical engineering-time series analysis”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 31 lipca 2025

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1

Rawizza, Mark Alan. "Time-series analysis of multivariate manufacturing data sets." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10895.

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Singh, 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.

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Lee, 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.

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Hutchinson, 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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.<br>Includes bibliographical references (p. 153-159).<br>by James M. Hutchinson.<br>Ph.D.

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5

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.

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Dzunic, 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.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 153-157).<br>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 uncer

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7

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.

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In this report six different models for predicting the electrical spot price on the Nordic power exchange, Nord Pool, are developed and compared. They are evaluated against the already existing model as well as the naive test, which is a forecast where the last week’s observations are used as a prognosis for the coming week. The models developed are constructed so that the models for different time resolutions are combined to create a full model. Harmonic regression with a linear trend are used to identify a yearly trend while SARIMAX/SARIMA time series models are used on a daily and hourly ba

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8

Thungtong, 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.

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Shashidhar, 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.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (leaves 133-150).<br>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 describin

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10

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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 183-190).<br>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

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11

Li, Chang. "Complexity Analysis of Physiological Time Series with Applications to Neonatal Sleep Electroencephalogram Signals." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1345657829.

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Mohajer, Maryam. "Nonlinear time series analysis of electrical activity in a slice model of epilepsy." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ46205.pdf.

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Fourie, Gert. "Power system stabilizer and controlled series capacitor small-signal stability performance analysis." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/53013.

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Thesis (MScEng)--University of Stellenbosch, 2002.<br>ENGLISH ABSTRACT: This thesis presents results of a study on the small-signal stability of a single-machine infinite-bus power system. Conditions of generator loading and network impedance are identified that require additional stability support. Two methods of stability enhancement are investigated, namely the power system stabilizer and the controlled series capacitor. Both stabilizers employ the conventional (classic) control structure, and parameters are evaluated for optimum performance using an integral-of-the-squared-error-base

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14

Meliane, Walid. "Applied time series analysis for forecasting process cycle times and process yields in the semiconductor manufacturing industry." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10331.

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Complementary metal oxide semiconductor (CMOS) integrated circuits (ICs) are the dominant technology in the semiconductor industry. Meeting delivery date is important to ensure customer satisfaction and maintain momentum between manufacturing divisions. Two major concerns are to produce the right quantity of ICs in the expected period of time. Yield and cycle time are two critical parameters used to assess process performance. The main objectives of this work were to obtain reliable forecasts of yields and cycle times and to monitor the process for detection of upsets. We explored the use of t

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15

Glaser, John Stanley 1964. "Analysis and design of a constant frequency diode-clamped series resonant converter." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/278060.

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A constant frequency diode-clamped series resonant power converter (CFCSRC) is proposed as a solution to problems associated with frequency-controlled resonant converters. This converter has two resonant frequencies, and control is achieved by varying the relative time per switching cycle spent at each resonant frequency. Two zero-current-switching (ZCS) modes are examined and plotted in the output plane. Operating and mode boundaries are found and also plotted in the output plane. The output equation for the main mode is shown to be hyperbolic. Peak voltages are shown to be less than or equal

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16

Norouzi, Mehdi. "Tracking Long-Term Changes in Bridges using Multivariate Correlational Data Analysis." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570591.

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17

Olsson, Magnus. "Optimal regulating power market bidding strategies in hydropower systems." Licentiate thesis, Stockholm :, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-596.

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Ahmad, Imad Saleh. "Analysis of Intermodulation Distortion for MESFET Small-signal Amplifiers." PDXScholar, 1995. https://pdxscholar.library.pdx.edu/open_access_etds/4989.

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Using the nonlinear Volterra series representation, analytical expressions for the third-order intermodulation distortion power and intercept point for a MESFET small-signal amplifier are derived when its equivalent circuit is bilateral and includes the gate-to-drain capacitance (CgJ explicitly as a nonlinear element. Previously developed analytical expressions treated Cgd as a linear element or incorporated it as part of gate-to-source and drainto- source capacitances (Cgs and Cds). These new analytical expressions are then compared with experimental data and good agreement is obtained. The a

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Ghanavati, Goodarz. "Statistical Analysis of High Sample Rate Time-series Data for Power System Stability Assessment." ScholarWorks @ UVM, 2015. http://scholarworks.uvm.edu/graddis/333.

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The motivation for this research is to leverage the increasing deployment of the phasor measurement unit (PMU) technology by electric utilities in order to improve situational awareness in power systems. PMUs provide unprecedentedly fast and synchronized voltage and current measurements across the system. Analyzing the big data provided by PMUs may prove helpful in reducing the risk of blackouts, such as the Northeast blackout in August 2003, which have resulted in huge costs in past decades. In order to provide deeper insight into early warning signs (EWS) of catastrophic events in power syst

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20

Winicki, Elliott. "ELECTRICITY PRICE FORECASTING USING A CONVOLUTIONAL NEURAL NETWORK." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2126.

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Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with l

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21

Khalilnejad, Arash. "Data-Driven Evaluation of HVAC Systems in Commercial Buildings and Identification of Savings Opportunities." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1579662167776891.

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Pang, Yu. "Optimization of fixed-point circuits represented by Taylor Series and real-valued polynomials including analysis of precision and range." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=92401.

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Kyriazis, Panagiotis A. "Analysis and processing of mechanically stimulated electrical signals for the identification of deformation in brittle materials." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4604.

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The fracture of brittle materials is of utmost importance for civil engineering and seismology applications. A different approach towards the aim of early identification of fracture and the prediction of failure before it occurs is attempted in this work. Laboratory experiments were conducted in a variety of rock and cement based material specimens of various shapes and sizes. The applied loading schemes were cyclic or increasing and the specimens were tested to compression and bending type loading of various levels. The techniques of Pressure Stimulated Current and Bending Stimulated Current

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Wong, Wai-ping. "Critical analysis and time series forecasting of electrical energy use in university buildings a case study of the University of Hong Kong /." Click to view the E-thesis via HKU Scholars Hub, 2004. http://lookup.lib.hku.hk/lookup/bib/B37933929.

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Tang, Chen. "Forecasting Service Metrics for Network Services." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284505.

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As the size and complexity of the internet increased dramatically in recent years,the burden of network service management also became heavier. The need foran intelligent way for data analysis and forecasting becomes urgent. The wideimplementation of machine learning and data analysis methods provides a newway to analyze large amounts of data.In this project, I study and evaluate data forecasting methods using machinelearning techniques and time series analysis methods on data collected fromthe KTH testbed. Comparing different methods with respect to accuracy andcomputing overhead I propose th

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Van, der Merwe Johannes Wilhelm (Wim). "Natural balancing mechanisms in converters." Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/6791.

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Thesis (PhD (Electrical and Electronic Engineering))--University of Stellenbosch, 2011.<br>AFRIKAANSE OPSOMMING: Hierdie proefskrif handel oor die natuurlike balanserings meganismes van veelvlakkige en modulêre omsetters wat fase-skuif dragolf puls wydte modulasie gebruik. Die meganismes kan in twee hoof groepe verdeel word: ‘n swak balanserings meganisme wat afhanklik is van die oorvleuling van die skakelfunksies en ‘n sterk meganisme wat voorkom ongeag of die skakelfunksies oorvleul al dan nie. Die sterk meganisme verdeel verder in twee subgroepe, ‘n direkte oordrag van onbalans energi

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27

Fatah, Kiar, and Taariq Nazar. "Stock Market Prediction With Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293853.

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Due to the unpredictability of the stock market,forecasting stock prices is a challenging task. In this project,we will investigate the performance of the machine learningalgorithm LSTM for stock market prediction. The algorithmwill be based only on historical numerical data and technicalindicators for IBM and FORD. Furthermore, the denoising anddimension reduction algorithm, PCA, is applied to the stockdata, to examine if the performance of forecasting the stockprice is greater than the initial model. A second method, transferlearning, is applied by training the model on the IBM datasetand th

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Wang, Hongjie. "Design and Control of Series Resonant Converters for DC Current Power Distribution Applications." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7160.

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With the growth of renewable energy usage and energy storage adoption in recent decades, people have started to reevaluate the possible roles of dc systems in current and future electrical systems. The dc voltage distribution has been applied in various applications, such as data centers and aircraft industry, for high efficiency and power density. However, for some applications such as subsea gas and oil fields, and ocean observatory systems, the dc current distribution is preferred over dc voltage distribution for its low cost and robustness against cable faults. Design and control of dc pow

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29

Hansson, Jörgen. "Analysis and control of a hybrid vehicle powered by free-piston energy converter." Licentiate thesis, KTH, School of Electrical Engineering (EES), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4189.

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<p>The introduction of hybrid powertrains has made it possible to utilise unconventional engines as primary power units in vehicles. The free-piston energy converter (FPEC) is such an engine. It is a combination of a free-piston combustion engine and a linear electrical machine. The main features of this configuration are high efficiency and a rapid transient response.</p><p>In this thesis the free-piston energy converter as part of a hybrid powertrain is studied. One issue of the FPEC is the generation of pulsating power due to the reciprocating motion of the translator. These pulsations affe

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Nigrini, Lucas Bernardo. "Developing a neural network model to predict the electrical load demand in the Mangaung municipal area." Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2012. http://hdl.handle.net/11462/176.

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Thesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012<br>Because power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers.

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Naeem, Muhammad Farhan. "Analysis of an Ill-posed Problem of Estimating the Trend Derivative Using Maximum Likelihood Estimation and the Cramér-Rao Lower Bound." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-95163.

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The amount of carbon dioxide in the Earth’s atmosphere has significantly increased in the last few decades as compared to the last 80,000 years approximately. The increase in carbon dioxide levels are affecting the temperature and therefore need to be understood better. In order to study the effects of global events on the carbon dioxide levels, one need to properly estimate the trends in carbon dioxide in the previous years. In this project, we will perform the task of estimating the trend in carbon dioxide measurements taken in Mauna Loa for the last 46 years, also known as the Keeling Curve

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32

Foghammar, Nömtak Carl. "Automatic SLAMS detection and magnetospheric classification in MMS data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285533.

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Short Large-Amplitude Magnetic Structures (SLAMS) have been observedby spacecraft near Earth’s quasi-parallel bow shock. They arecharacterized by a short and sudden increase of the magnetic field,usually by a factor of 2 or more. SLAMS studies have previously beenlimited to small sample sizes because SLAMS were identified throughmanual inspection of the spacecraft data. This makes it difficult to drawgeneral conclusions and the subjective element complicates collaborationbetween researchers. A solution is presented in this thesis; anautomatic SLAMS detection algorithm. We investigate several m

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Lorgat, Mohamed Wasim. "Detecting network attacks using high-resolution time series." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29489.

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Research in the detection of cyber-attacks has sky-rocketed in the recent past. However, there remains a striking gap between usage of the proposed algorithms in academic research versus industrial applications. Leading researchers have argued that efforts toward the understanding of proposed detectors are lacking. By digging deeper into their inner workings and critically evaluating their underlying assumptions, better detectors may be built. The aim of this thesis is therefore to provide an underlying theory for understanding a single class of detection algorithms, in particular, anomaly-bas

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Ayyaz, Usman. "Time series formalism : a systems approach." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112896.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 51-52).<br>Time series data has become a modern day phenomena: from stock market data to social media information, modern day data exists as a continuous flow of information indexed by timestamps. Using this data to gather contextual inference and make future predictions is vital to gaining an analytical edge. While there are specialized time series databases and libraries available that o

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Gartheeban, Ganeshapillai. "Learning connections in financial time series." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/93061.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 125-132).<br>Much of modern financial theory is based upon the assumption that a portfolio containing a diversified set of equities can be used to control risk while achieving a good rate of return. The basic idea is to choose equities that have high expected returns, but are unlikely to move together. Identifying a portfolio of equities that remain well diversified over a future investment

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Liao, Ruizhi. "Temporal registration for MRI time series." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111921.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 29-32).<br>Time-course analysis in medical image series often suffers from serious motion. Registration provides voxel correspondences among images, and is commonly employed for correcting motion in medical images. Yet, the registration procedure fails when aligning volumes that are substantially different from template. We present a robust method to correct for motion and deformations in MRI

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Hung, Elmer S. "Parameter estimation in chaotic time series." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/32609.

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Mashikian, Paul Stephan. "Multiresolution models of financial time series." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43483.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (leaves 89-92).<br>by Paul Stephan Mashikian.<br>M.Eng.

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Thornlow, Robert Timothy. "Spectrum estimation using extrapolated time series." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA246554.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 1990.<br>Thesis Advisor(s): Hippenstiel, Ralph. Second Reader: Tummala, Murali. "December 1990." Description based on title screen as viewed on March 30, 2010. DTIC Descriptor(s): Frequency, Density, Data Management, Models, Signal To Noise Ratio, Theses, Power Spectra, Sequences, Estimates, Short Range(Time), Spectra, Sampling, Fast Fourier Transforms, Extrapolation, Data Processing. DTIC Identifier(s): Power Spectra, Estimates, Time Series Analysis, Extrapolation, Density, Theses, Fast Fourier Transforms, Eigenvecto

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Johnson, Matthew James Ph D. Massachusetts Institute of Technology. "Bayesian time series models and scalable inference." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89993.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 197-206).<br>With large and growing datasets and complex models, there is an increasing need for scalable Bayesian inference. We describe two lines of work to address this need. In the first part, we develop new algorithms for inference in hierarchical Bayesian time series models based on the hidden Markov model (HMM), hidden semi-Markov model (HSMM), and their Bayesian nonparametric extens

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Tinawi, Ihssan. "Machine learning for time series anomaly detection." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123129.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 55).<br>In this thesis, I explored machine learning and other statistical techniques for anomaly detection on time series data obtained from Internet-of-Things sensors. The data, obtained from satellite telemetry signals, were

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Gerber, Georg Kurt 1970. "Do the time-wrap: continuous alignment of gene expression time-series data." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87336.

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Grullon, Dylan Emanuel Centeno. "Disentangling time constant and time dependent hidden state in time series with variational Bayesian inference." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/124572.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 85-86).<br>In this thesis, we design and explore a new model architecture called a Variational Bayes Recurrent Neural Network (VBRNN) for modelling time series. The VBRNN contains explicit structure to disentangle time constan

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Gerstein, Beth Tamara. "Analyzing biololgical time series with higher order cumulants." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43403.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (p. 103-105).<br>by Beth Tamara Gerstein.<br>M.S.

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Pienaar, Marc. "On the classification of time series and cross wavelet phase variance." Doctoral thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/22869.

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The continuous wavelet transform (CWT) is arguably one of the best tools to explore underlying characteristic features of time series data. Its application in large time series classification experiments, however, has been severely limited due to the large amount of redundant associated information. By extending the capabilities of the CWT to perform cross wavelet analysis (CWA), common frequency behaviour between two time series is highlighted, and the potential to extract amplitude modulated (AM) and frequency modulation (FM) characteristics in an automated way is possible. Characterisation

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Jain, Bonny. "Modelling signal interactions with application to financial time series." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91832.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Thesis: S.B., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 99-100).<br>In this thesis, we concern ourselves with the problem of reasoning over a set of objects evolving over time that are coupled through interaction structures that are themselves changing over time. We focus on inferring time-varying interaction structures among

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Rahimi, Ali 1976. "Learning to transform time series with a few examples." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/35528.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.<br>Also issued as printed in pages.<br>MIT Barker Engineering Library copy: printed in pages.<br>Includes bibliographical references (leaves 113-119).<br>I describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. I apply this algorithm to tracking, where one transforms a time series of observations from sensors to a time series describing the pose of a target. Instead of defini

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Eriksson, Henrik, and Victor Löfgren. "Evaluating Different Algorithms for Detecting Change-points in Time Series." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239360.

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Change-point detection is useful in many areas and there are several algorithms developed to cater specific needs such as fast calculations and statistical accuracy. In this report we outline five different algorithms, namely Fused Lasso, Filtered Fused Lasso, Normalized Fused Lasso, Dynamic Programming Approach and our own creation using convolution filtering, the Convolution Method. We evaluate them in the mentioned areas. Convolution Method is the fastest, Dynamic Programming Approach is the most accurate but suffers greatly in speed compared the other algorithms. Normalized and Filtered Fu

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Åkerlund, Agnes. "Time-Series Analysis of Pulp Prices." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39726.

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The pulp and paper industry has a significant role in Europe’s economy and society, and its significance is still growing. The pulp market and the customers’ requirements are highly affected by the pulp market prices and the requested kind of pulp, i.e., Elementary Chlorine Free (ECF) or Total Chlorine Free (TCF). There is a need to predict different market aspects, where the market price is one, to gain a better understanding of a business situation. Understanding market dynamics can support organizations to optimize their processes and production. Forecasting future pulp prices has not recen

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Kim, Yongwook Bryce. "Physiological time series retrieval and prediction with locality-sensitive hashing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111903.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 131-143).<br>The amount of time series data collected in the medical community has recently been exploding due to widespread affordable sensors and storage devices. However, while the massive repositories of such physiological time series data provide enormous opportunities for machine learning to make significant impacts, they are largely under-utilized due to their granular detail, overwh

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