Dissertations / Theses on the topic 'SPSS series in data analysis'
To see the other types of publications on this topic, follow the link: SPSS series in data analysis.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'SPSS series in data analysis.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Brunsdon, T. M. "Time series analysis of compositional data." Thesis, University of Southampton, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378257.
Full text
2
Guthrey, Delparde Raleigh. "Time series analysis of ozone data." CSUSB ScholarWorks, 1998. https://scholarworks.lib.csusb.edu/etd-project/1788.
Full text
3
Clarke, Liam. "Nonlinear time series analysis of data streams." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401147.
Full text
4
Jiang, Chunyu. "DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264.
Full text
5
Mazel, David S. "Fractal modeling of time-series data." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/13916.
Full text
6
Rawizza, Mark Alan. "Time-series analysis of multivariate manufacturing data sets." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10895.
Full text
7
Rodrigues, David Francis. "Modelling election poll data using time series analysis." Thesis, University of Kent, 2009. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527582.
Full text
8
Leung, Caleb Chee Shan. "Time series modelling of birth data." Thesis, Canberra, ACT : The Australian National University, 1995. http://hdl.handle.net/1885/118134.
Full textAbstract:
Three basic methods namely cohort component projection methods, statistical time series
methods and structural modelling methods are discussed for the purpose of forecasting
births, with the main focus on univariate time series methods. A general autoregressive
integrated moving average model for birth time series is developed from the
mathematical demographic renewal equation for births. The four-stage Box-Jenkins
modelling method of model identification, estimation, diagnosis and forecasting is
investigated in detail. This method is employed to model and forecast Australian birth
time series. Finally, the comparison between time series forecasts and cohort component
projections of births for Australia is made.
9
Wan, Xiaogeng. "Time series causality analysis and EEG data analysis on music improvisation." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/23956.
Full textAbstract:
This thesis describes a PhD project on time series causality analysis and applications. The project is motivated by two EEG measurements of music improvisation experiments, where we aim to use causality measures to construct neural networks to identify the neural differences between improvisation and non-improvisation. The research is based on mathematical backgrounds of time series analysis, information theory and network theory. We first studied a series of popular causality measures, namely, the Granger causality, partial directed coherence (PDC) and directed transfer function (DTF), transfer entropy (TE), conditional mutual information from mixed embedding (MIME) and partial MIME (PMIME), from which we proposed our new measures: the direct transfer entropy (DTE) and the wavelet-based extensions of MIME and PMIME. The new measures improved the properties and applications of their father measures, which were verified by simulations and examples. By comparing the measures we studied, MIME was found to be the most useful causality measure for our EEG analysis. Thus, we used MIME to construct both the intra-brain and cross-brain neural networks for musicians and listeners during the music performances. Neural differences were identified in terms of direction and distribution of neural information flows and activity of the large brain regions. Furthermore, we applied MIME on other EEG and financial data applications, where reasonable causality results were obtained.
10
Perez, Melo Sergio. "Statistical Analysis of Meteorological Data." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1527.
Full textAbstract:
Some of the more significant effects of global warming are manifested in the rise of temperatures and the increased intensity of hurricanes. This study analyzed data on Annual, January and July temperatures in Miami in the period spanning from 1949 to 2011; as well as data on central pressure and radii of maximum winds of hurricanes from 1944 to present.
Annual Average, Maximum and Minimum Temperatures were found to be increasing with time. Also July Average, Maximum and Minimum Temperatures were found to be increasing with time. On the other hand, no significant trend could be detected for January Average, Maximum and Minimum Temperatures.
No significant trend was detected in the central pressures and radii of maximum winds of hurricanes, while the radii of maximum winds for the largest hurricane of the year showed an increasing trend.
11
Liang, Hong. "Adaptive Fourier Analysis For Unequally-Spaced Time Series Data." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27722.
Full textAbstract:
Fourier analysis, Walsh-Fourier analysis, and wavelet analysis have often been used in time series analysis. Fourier analysis can be used to detect periodic components that have sinusoidal shape; however, it might be misleading when the periodic components are not sinusoidal. Walsh-Fourier analysis is suitable for revealing the rectangular trends of time series. The flaw of the Walsh-Fourier analysis is that Walsh functions are not periodic. The resulting Walsh-Fourier analysis is more difficult to interpret than classical Fourier analysis. Wavelet analysis is very useful in analyzing and describing time series with gradual frequency changes. Wavelet analysis also has a shortcoming by giving no exact meaning to the concept of frequency because wavelets are not periodic functions. In addition, all three analysis methods above require equally-spaced time series observations.
In this dissertation, by using a sequence of periodic step functions, a new analysis method, adaptive Fourier analysis, and its theory are developed. These can be applied to time series data where patterns may take general periodic shapes that include sinusoids as special cases. Most importantly, the resulting adaptive Fourier analysis does not require equally-spaced time series observations.Ph. D.
12
Venugopal, Niveditha. "Annotation-Enabled Interpretation and Analysis of Time-Series Data." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4708.
Full textAbstract:
As we continue to produce large amounts of time-series data, the need for data analysis is growing rapidly to help gain insights from this data. These insights form the foundation of data-driven decisions in various aspects of life. Data annotations are information about the data such as comments, errors and provenance, which provide context to the underlying data and aid in meaningful data analysis in domains such as scientific research, genomics and ECG analysis. Storing such annotations in the database along with the data makes them available to help with analysis of the data. In this thesis, I propose a user-friendly technique for Annotation-Enabled Analysis through which a user can employ annotations to help query and analyze data without having prior knowledge of the details of the database schema or any kind of database programming language. The proposed technique receives the request for analysis as a high-level specification, hiding the details of the schema, joins, etc., and parses it, validates the input and converts it into SQL. This SQL query can then be executed in a relational database and the result of the query returned to the user. I evaluate this technique by providing real-world data from a building-data platform containing data about Portland State University buildings such as room temperature, air volume and CO2 level. This data is annotated with information such as class schedules, power outages and control modes (for example, day or night mode). I test my technique with three increasingly sophisticated levels of use cases drawn from this building science domain. (1) Retrieve data with include or exclude annotation selection (2) Correlate data with include or exclude annotation selection (3) Align data based on include annotation selection to support aggregation over multiple periods. I evaluate the technique by performing two kinds of tests: (1) To validate correctness, I generate synthetic datasets for which I know the expected result of these annotation-enabled analyses and compare the expected results with the results generated from my technique (2) I evaluate the performance of the queries generated by this service with respect to execution time in the database by comparing them with alternative SQL translations that I developed.
13
丁嘉慧 and Ka-wai Ting. "Time sequences: data mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226760.
Full text
14
Heinen, Andreas. "Modelling time series counts data in financial microstructure /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3130202.
Full text
15
Stark, J. Alex. "Statistical model selection techniques for data analysis." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390190.
Full text
16
Zoltan, Geler. "Role of Similarity Measures in Time Series Analysis." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2015. https://www.cris.uns.ac.rs/record.jsf?recordId=94848&source=NDLTD&language=en.
Full textAbstract:
The subject of this dissertation encompasses a comprehensive overviewand analysis of the impact of Sakoe-Chiba global constraint on the mostcommonly used elastic similarity measures in the field of time-series datamining with a focus on classification accuracy. The choice of similaritymeasure is one of the most significant aspects of time-series analysis - itshould correctly reflect the resemblance between the data presented inthe form of time series. Similarity measures represent a criticalcomponent of many tasks of mining time series, including: classification,clustering, prediction, anomaly detection, and others.The research covered by this dissertation is oriented on several issues:1. review of the effects of global constraints on theperformance of computing similarity measures,2. a detailed analysis of the influence of constraining the elasticsimilarity measures on the accuracy of classical classificationtechniques,3. an extensive study of the impact of different weightingschemes on the classification of time series,4. development of an open source library that integrates themain techniques and methods required for analysis andmining time series, and which is used for the realization ofthese experimentsPredmet istraživanja ove disertacije obuhvata detaljan pregled i analizu uticaja Sakoe-Chiba globalnog ograničenja na najčešće korišćene elastične mere sličnosti u oblasti data mining-a vremenskih serija sa naglaskom na tačnost klasifikacije. Izbor mere sličnosti jedan je od najvažnijih aspekata analize vremenskih serija - ona treba verno reflektovati sličnost između podataka prikazanih u obliku vremenskih serija. Mera sličnosti predstavlјa kritičnu komponentu mnogih zadataka mining-a vremenskih serija, uklјučujući klasifikaciju, grupisanje (eng. clustering), predviđanje, otkrivanje anomalija i drugih.Istraživanje obuhvaćeno ovom disertacijom usmereno je na nekoliko pravaca:1. pregled efekata globalnih ograničenja na performanse računanja mera sličnosti,2. detalјna analiza posledice ograničenja elastičnih mera sličnosti na tačnost klasifikacije klasičnih tehnika klasifikacije,3. opsežna studija uticaj različitih načina računanja težina (eng. weighting scheme) na klasifikaciju vremenskih serija,4. razvoj biblioteke otvorenog koda (Framework for Analysis and Prediction - FAP) koja će integrisati glavne tehnike i metode potrebne za analizu i mining vremenskih serija i koja je korišćena za realizaciju ovih eksperimenata.
Predmet istraživanja ove disertacije obuhvata detaljan pregled i analizu uticaja Sakoe-Chiba globalnog ograničenja na najčešće korišćene elastične mere sličnosti u oblasti data mining-a vremenskih serija sa naglaskom na tačnost klasifikacije. Izbor mere sličnosti jedan je od najvažnijih aspekata analize vremenskih serija - ona treba verno reflektovati sličnost između podataka prikazanih u obliku vremenskih serija. Mera sličnosti predstavlja kritičnu komponentu mnogih zadataka mining-a vremenskih serija, uključujući klasifikaciju, grupisanje (eng. clustering), predviđanje, otkrivanje anomalija i drugih.Istraživanje obuhvaćeno ovom disertacijom usmereno je na nekoliko pravaca:1. pregled efekata globalnih ograničenja na performanse računanja mera sličnosti,2. detaljna analiza posledice ograničenja elastičnih mera sličnosti na tačnost klasifikacije klasičnih tehnika klasifikacije,3. opsežna studija uticaj različitih načina računanja težina (eng. weighting scheme) na klasifikaciju vremenskih serija,4. razvoj biblioteke otvorenog koda (Framework for Analysis and Prediction - FAP) koja će integrisati glavne tehnike i metode potrebne za analizu i mining vremenskih serija i koja je korišćena za realizaciju ovih eksperimenata.
17
Bosque, Edward F. "Time series analysis of RTC Great Lakes recruit graduate data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA359541.
Full text
18
Kaffashi, Farhad. "Variability analysis & its applications to physiological time series data." online version, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1181072302.
Full text
19
彭運佳 and Wan-kai Pang. "Time series analysis of meteorological data: wind speed and direction." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B30425979.
Full text
20
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.
Full text
21
Pang, Wan-kai. "Time series analysis of meteorological data : wind speed and direction /." [Hong Kong] : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13456933.
Full text
22
Yagoubi, Djamel edine. "Indexing and analysis of very large masses of time series." Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS084/document.
Full textAbstract:
Les séries temporelles sont présentes dans de nombreux domaines d'application tels que la finance, l'agronomie, la santé, la surveillance de la Terre ou la prévision météorologique, pour n'en nommer que quelques-uns. En raison des progrès de la technologie des capteurs, de telles applications peuvent produire des millions, voir des des milliards, de séries temporelles par jour, ce qui nécessite des techniques rapides d'analyse et de synthèse.Le traitement de ces énormes volumes de données a ouvert de nouveaux défis dans l'analyse des séries temporelles. En particulier, les techniques d'indexation ont montré de faibles performances lors du traitement des grands volumes des données.Dans cette thèse, nous abordons le problème de la recherche de similarité dans des centaines de millions de séries temporelles. Pour cela, nous devons d'abord développer des opérateurs de recherche efficaces, capables d'interroger une très grande base de données distribuée de séries temporelles avec de faibles temps de réponse. L'opérateur de recherche peut être implémenté en utilisant un index avant l'exécution des requêtes.L'objectif des indices est d'améliorer la vitesse des requêtes de similitude. Dans les bases de données, l'index est une structure de données basées sur des critères de recherche comme la localisation efficace de données répondant aux exigences. Les index rendent souvent le temps de réponse de l'opération de recherche sous linéaire dans la taille de la base de données. Les systèmes relationnels ont été principalement supportés par des structures de hachage, B-tree et des structures multidimensionnelles telles que R-tree, avec des vecteurs binaires jouant un rôle de support. De telles structures fonctionnent bien pour les recherches, et de manière adéquate pour les requêtes de similarité. Nous proposons trois solutions différentes pour traiter le problème de l'indexation des séries temporelles dans des grandes bases de données. Nos algorithmes nous permettent d'obtenir d'excellentes performances par rapport aux approches traditionnelles.Nous étudions également le problème de la détection de corrélation parallèle de toutes paires sur des fenêtres glissantes de séries temporelles. Nous concevons et implémentons une stratégie de calcul incrémental des sketchs dans les fenêtres glissantes. Cette approche évite de recalculer les sketchs à partir de zéro. En outre, nous développons une approche de partitionnement qui projette des sketchs vecteurs de séries temporelles dans des sous-vecteurs et construit une structure de grille distribuée. Nous utilisons cette méthode pour détecter les séries temporelles corrélées dans un environnement distribuéTime series arise in many application domains such as finance, agronomy, health, earth monitoring, weather forecasting, to name a few. Because of advances in sensor technology, such applications may produce millions to trillions of time series per day, requiring fast analytical and summarization techniques.The processing of these massive volumes of data has opened up new challenges in time series data mining. In particular, it is to improve indexing techniques that has shown poor performances when processing large databases.In this thesis, we focus on the problem of parallel similarity search in such massive sets of time series. For this, we first need to develop efficient search operators that can query a very large distributed database of time series with low response times. The search operator can be implemented by using an index constructed before executing the queries. The objective of indices is to improve the speed of data retrieval operations. In databases, the index is a data structure, which based on search criteria, efficiently locates data entries satisfying the requirements. Indexes often make the response time of the lookup operation sublinear in the database size.After reviewing the state of the art, we propose three novel approaches for parallel indexing and queryin large time series datasets. First, we propose DPiSAX, a novel and efficient parallel solution that includes a parallel index construction algorithm that takes advantage of distributed environments to build iSAX-based indices over vast volumes of time series efficiently. Our solution also involves a parallel query processing algorithm that, given a similarity query, exploits the available processors of the distributed system to efficiently answer the query in parallel by using the constructed parallel index.Second, we propose RadiusSketch a random projection-based approach that scales nearly linearly in parallel environments, and provides high quality answers. RadiusSketch includes a parallel index construction algorithm that takes advantage of distributed environments to efficiently build sketch-based indices over very large databases of time series, and then query the databases in parallel.Third, we propose ParCorr, an efficient parallel solution for detecting similar time series across distributed data streams. ParCorr uses the sketch principle for representing the time series. Our solution includes a parallel approach for incremental computation of the sketches in sliding windows and a partitioning approach that projects sketch vectors of time series into subvectors and builds a distributed grid structure.Our solutions have been evaluated using real and synthetics datasets and the results confirm their high efficiency compared to the state of the art
23
Becker, Ralf. "Testing for nonlinear structure in time-series data." Thesis, Queensland University of Technology, 2001.
Find full text
24
Freeland, R. Keith. "Statistical analysis of discrete time series with application to the analysis of workers' compensation claims data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq27144.pdf.
Full text
25
Bai, Mingyuan. "Learning from Tensors: Tensor Learning for Tensorial Data Analysis." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29424.
Full textAbstract:
Data with rich spatial information are commonly acquired in the real-world. These data are often represented by multi-way arrays, i.e., tensors. For those also with temporal information, they can be sketched as tensorial time series. Tensorial data, including tensorial time series are closely related to “big data”, because they are often with the “4V” features where data are in the large volumes and variety, require the high velocity to process them and can be with veracity caused by outliers, noises, missing values, etc., in practice. Existing methods either flatten tensors into vectors or impose strong assumptions. The former can cause the extreme large number of parameters and fail to process large volume data with the high velocity, whereas the latter cannot effectively deal with the challenge from the veracity and variety.
This thesis consists of three topics which can form a pipeline to analyse tensorial data, including tensorial time series, with efficacy and other desired characteristics, to address the “4V” features. Firstly, for data preprocessing, we proposed a dimensionality reduction model Tensor-Train Parameterisation for Ultra Dimensionality Reduction (TTPUDR) specifically for ultra-dimensional data which are converted from tensors. Also, they have dimensions larger than the number of samples, which violates the assumption of many past methods. TTPUDR efficiently and effectively captures complicated spatial information in these data, avoids the curse-of-dimensionality problem and copes with extreme outliers. In the second and the third topics, we proposed a series of tensor neural differential equations to exploit complicated nonlinear spatial and temporal information for tensorial time series prediction, including irregular ones with unequally-spaced time steps which violate the equidistance assumption on time steps of many existing methods. For models proposed in all three topics, their efficacy is proved with theoretical guarantees. In numerical experiments, all proposed models outperform the existing models and demonstrate their efficiency and effectiveness on complicated spatial information and/or temporal information analysis in tensorial data, including tensorial time series.
26
Hempel, Sabrina. "Deciphering gene regulation from time series data." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012. http://dx.doi.org/10.18452/16602.
Full textAbstract:
Meine Arbeit beschäftigt sich mit der Rekonstruktion genregulatorischer Netze, um die Funktionalität von Organismen und ihre Reaktionen auf die vielfältigen externen Einflussfaktoren besser zu verstehen. Die Analyse kurzer, zeitaufgelöster Daten mit Hilfe von Assoziationsmaßen kann dabei erste wesentliche Einblicke in mögliche Wechselwirkungskreisläufe liefern. In einer umfangreicher Vergleichstudie untersuche ich die Effizienz der Netzwerkrekonstruktion bei der Anwendung verschiedener Maße und Bewertungsschemata. Weiterhin führe ich IOTA (inner composition alignment) als ein neues asymmetrisches, permutationsbasiertes Ähnlichkeitsmaß ein, welches ein effektives Werkzeug zur Rekonstruktion gerichteter Netzwerke ohne die Verwendung zusätzlicher Bewertungsschemata darstellt. In meiner Arbeit betrachte ich verschiedene Modifikationen dieses Maßes und untersuche deren Eigenschaften. Dabei zeige ich, dass IOTA geeignet ist, um statistisch signifikante gerichtete, nichtlineare Kopplungen in verschiedenen Zeitreihen (autoregressive Prozesse, Michaelis-Menten Kinetik und chaotische Oszillatoren in verschiedenen Regimen) und Autoregulation zu untersuchen. Weiterhin erlaubt IOTA, ebenso wie die Korrelationsmaße, die Spezifizierung des Types der Regulation (Aktivierung oder Repression), was es zu dem einzigen Maß macht, dass die Ableitung aller für die Rekonstruktion genregulatorischer Netzwerke erforderlichen Kenndaten ermöglicht. Schließlich nutze ich das neuen Ähnlichkeitsmaß IOTA, um ein genregulatorisches Netzwerk für die Grünalgenart Chlamydomonas reinhardtii unter Kohlenstoffmangel aus experimentellen Daten abzuleiten.My thesis is about reconstructing gene regulatory networks in order to better understand the functionality of organisms and their reactions to various external influences. In this context, the analysis of short, time-resolved measurements with association measures can yield crucial insights into possible interactions. In an extensive comparison study, I examine the efficiency of different measures and scoring schemes for solving the network reconstruction problem. Furthermore, I introduce IOTA (inner composition alignment), a novel asymmetric, permutation-based association measure, as an efficent tool for reconstructing directed networks without the application of additional scoring schemes. In my thesis, I analyze the properties of various modifications of the measure. Moreover, I show that IOTA is valuable to study significant, directed, nonlinear couplings in several time series (autoregressive processes, Michaelis-Menten kinetics and chaotic oscillators in different dynamical regimes) , as well as autoregulation. In addition, IOTA, similar to correlation measures, permits to identify the type of regulation (activation or repression). Hence, it is the only measure that can determine all necessay characteristics when reconstruction regulatory networks. Finally, I apply the novel association measure IOTA to infer a gene regulatory network for the green algae Chlamydomas reinhardtii under carbon deprivation from experimally obtained data.
27
Xiong, Yimin. "Time series clustering using ARMA models /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20XIONG.
Full textAbstract:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.Includes bibliographical references (leaves 49-55). Also available in electronic version. Access restricted to campus users.
28
Wahrman, Spencer A. "Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17473.
Full textAbstract:
Approved for public release; distribution is unlimitedGrand Lake, Colorado has experienced a severe mountain pine beetle outbreak over the past twenty years. The aim of this study was to map lodgepole pine mortality and health decline due to mountain pine beetle. Multispectral data spanning a five-year period from 2006 to 2011 were used to assess the progression from live, green trees to dead, gray-brown trees. IKONOS data from 2011 were corrected to reflectance and validated against an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset, also collected during 2011. These data were used along with additional reflectance-corrected multispectral datasets (IKONOS from 2007 and QuickBird from 2006 and 2009) to create vegetation classification maps using both library spectra and regions of interest. Two sets of classification maps were produced using Mixture-Tuned Matched Filtering. The results were assessed visually and mathematically. Through visual inspection of the classification maps, increasing lodgepole pine mortality over time was observed. The results were quantified using confusion matrices comparing the classification results of the AVIRIS classified data and the IKONOS and QuickBird classified data. The comparison showed that change could be seen over time, but due to the short time period of the data the change was not as significant as expected.
29
Belcher, John. "Topics in the time series analysis of medical and psychological data." Thesis, Keele University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362162.
Full textAbstract:
The thesis gives examples of analysing time series data while being employed as statistician at the Industrial and Community Health Research Centre, North Staffordshire Medical Institute, Hartshill Road, Stoke-on-Trent for the period 1988-1997. Topics include:(a) the analysis of asthma data with a view to aid detection and confirmation of occupational asthma. This project highlights possible approaches for modelling regularly and unequally spaced observations (b) modelling bleeding and behavioural patterns of handicapped people using binary valued time series (c) a repeated measures analysis following a surgical intervention (d) a study relating mood scores to progesterone levels
30
Al-Hitmi, Mohammed Abdulla E. "Non-linear data analysis and neural networks for time series prediction." Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370084.
Full text
31
Brooks, Evan B. "Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23276.
Full textAbstract:
Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth's land surface and vegetation. There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate.
Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements. In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001. Harmonic regression had an R^2"0.9 over three quarters of all pixels. It gave the highest R_Predicted^2 values on two thirds of the pixels. Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R^2"0.99 for most pixels.
We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data. The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm. EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery.
We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values. These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential. These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters. Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase.
These applications demonstrate the utility of harmonic regression for Landsat data. They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects.Ph. D.
32
Lange, Mona [Verfasser]. "Time series data mining for context-aware event analysis / Mona Lange." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2017. http://d-nb.info/114264801X/34.
Full text
33
Stoecker-Sylvia, Zachary. "Mining for frequent events in time series." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0902104-163011/.
Full text
34
Pradhan, Shameer Kumar. "Investigation of Event-Prediction in Time-Series Data : How to organize and process time-series data for event prediction?" Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19416.
Full textAbstract:
The thesis determines the type of deep learning algorithms to compare for a particular dataset that contains time-series data. The research method includes study of multiple literatures and conduction of 12 tests. It deals with the organization and processing of the data so as to prepare the data for prediction of an event in the time-series. It also includes the explanation of the algorithms selected. Similarly, it provides a detailed description of the steps taken for classification and prediction of the event. It includes the conduction of multiple tests for varied timeframe in order to compare which algorithm provides better results in different timeframes. The comparison between the selected two deep learning algorithms identified that for shorter timeframes Convolutional Neural Networks performs better and for longer timeframes Recurrent Neural Networks has higher accuracy in the provided dataset. Furthermore, it discusses possible improvements that can be made to the experiments and the research as a whole.
35
Deng, Cheng. "Time Series Decomposition Using Singular Spectrum Analysis." Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etd/2352.
Full textAbstract:
Singular Spectrum Analysis (SSA) is a method for decomposing and forecasting time series that recently has had major developments but it is not yet routinely included in introductory time series courses. An international conference on the topic was held in Beijing in 2012. The basic SSA method decomposes a time series into trend, seasonal component and noise. However there are other more advanced extensions and applications of the method such as change-point detection or the treatment of multivariate time series. The purpose of this work is to understand the basic SSA method through its application to the monthly average sea temperature in a point of the coast of South America, near where “EI Ni˜no” phenomenon originates, and to artificial time series simulated using harmonic functions. The output of the basic SSA method is then compared with that of other decomposition methods such as classic seasonal decomposition, X-11 decomposition using moving averages and seasonal decomposition by Loess (STL) that are included in some time series courses.
36
Vannicola, Catherine Marie. "Analysis of medical time series data using phase space analysis a complex systems approach /." Diss., Online access via UMI:, 2007.
Find full text
37
Fu, Bo, and 傅博. "Some topics in longitudinal data analysis and panel time seriesmodels." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31244166.
Full text
38
Shakeri, Mohammad Taghi. "Statistical modelling of medical time series data : the dynamic sway magnetometry test." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369783.
Full text
39
Cancado, Luciana Pacheco. "Economic growth panel data evidence from Latin America /." Ohio : Ohio University, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1127143858.
Full text
40
Jones, Lewys. "Applications of focal-series data in scanning-transmission electron microscopy." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:a6f2a4d5-e77a-47a5-b2d7-aab4b7069ce2.
Full textAbstract:
Since its development, the scanning transmission electron microscope has rapidly found uses right across the material sciences. Its use of a finely focussed electron probe rastered across samples offers the microscopist a variety of imaging and spectroscopy signals in parallel. These signals are individually intuitive to interpret, and collectively immensely powerful as a research tool. Unsurprisingly then, much attention is concentrated on the optical quality of the electron probes used. The introduction of multi-pole hardware to correct optical distortions has yielded a step-change in imaging performance; now with spherical and other remnant aberrations greatly reduced, larger probe forming apertures are suddenly available. Probes formed by such apertures exhibit a much improved and routinely sub-Angstrom diffraction-limited resolution, as well as a greatly increased probe current for spectroscopic work. The superb fineness of the electron beams and enormous magnifications now achievable make the STEM instrument one of the most sensitive scientific instruments developed by man, and this thesis will deal with two core issues that suddenly become important in this new aberration-corrected era. With this new found sensitivity comes the risk of imaging-distortion from outside influences such as acoustic or mechanical vibrations. These can corrupt the data in an unsatisfactory manner and counter the natural interpretability of the technique. Methods to identify and diagnose this distortion will be discussed, and a new technique developed to restore the corrupted data presented. Secondly, the subtleties of probe-shape in the multi-pole corrected STEM are extensively evaluated via simulation, with the contrast-transfer capabilities across defocus explored in detail. From this investigation a new technique of STEM focal-series reconstruction (FSR) is developed to compensate for the small remnant aberrations that still persist – recovering the sample object function free from any optical distortion. In both cases the methodologies were developed into automated computer codes and example restorations from the two techniques are shown (separately, although in principal the scan-corrected output is compatible with FSR). The performance of these results has been quantified with respect to several factors including; image resolution, signal-noise ratio, sample-drift, low frequency instability, and quantitative image intensity. The techniques developed are offered as practical tools for the microscopist wishing to push the performance of their instrument just that little bit further.
41
Tsang, Fan Cheong. "Advances in flood forecasting using radar rainfalls and time-series analysis." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.481184.
Full textAbstract:
This thesis reports the use of a time-series analysis approach to study the catchment hydrological system of the River Ribble. Rain gauge records, radar rainfall estimates and flow data are used in the analysis. The preliminary study consists of the flow forecasting at Reedyford, Pendle Water (82 km2). Flow forecasts generated from the rain gauge records are better than the radar rainfall estimates over this small catchment. However, the catchment response to rainfall is quick and no clear advantages in extending the lead-time of the forecast can be introduced by using an artificial time delayed rainfall input. A non-linear rainfall-flow relationship has been studied using the rain gauge rainfall and flow records at the River Hodder catchment (261 km2). A calibration scheme is used to identify the non-linear function of the catchment as well as the rainfall-flow system model. Although a better time-invariant system model can be identified, the non-linear rainfall-flow process cannot be fully explained by a power law function of effective rainfall. Assuming the dynamic, nonlinear system characteristics of the catchment can be reflected by a time-varying model gain parameter, relationships between the parameter and the flow, and between the parameter and the rainfall can be evaluated. These relationships have been used to improve the flow forecast during storm events. The results indicate, however, that the approach failed to improve the flow forecast near the peak flow condition. Radar data have been incorporated to forecast the flow at Jumbles Rock (1053 km2) and Samlesbury (1140 km2), River Ribble. The radar data calibrated by the Lancaster University Adaptive Radar Calibration System appears to produce better flow forecasts than the standard radar data product calibrated by the Meteorological Office. The proposed flow forecasting scheme generates better forecasts than the current system operated by the National Rivers Authority, North West Region.
42
Abualhamayl, Abdullah Jameel Mr. "APPLY DATA CLUSTERING TO GENE EXPRESSION DATA." CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/259.
Full textAbstract:
Data clustering plays an important role in effective analysis of gene expression. Although DNA microarray technology facilitates expression monitoring, several challenges arise when dealing with gene expression datasets. Some of these challenges are the enormous number of genes, the dimensionality of the data, and the change of data over time. The genetic groups which are biologically interlinked can be identified through clustering. This project aims to clarify the steps to apply clustering analysis of genes involved in a published dataset. The methodology for this project includes the selection of the dataset representation, the selection of gene datasets, Similarity Matrix Selection, the selection of clustering algorithm, and analysis tool. R language with the focus of Kmeans, fpc, hclust, and heatmap3 packages in R is used in this project as an analysis tool. Different clustering algorithms are used on Spellman dataset to illustrate how genes are grouped together in clusters which help to understand our genetic behaviors.
43
Pang, Kwok-wing. "Statistical analysis of high frequency data using autoregressive conditional duration models /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2275314x.
Full text
44
Morton, Alexander Stuart. "Spectral analysis of irregularly sampled time series data using continuous time autoregressions." Thesis, Lancaster University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288870.
Full text
45
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.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes 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.
46
Aerts, Xing Qin. "Time Series Data Analysis of Single Subject Experimental Designs Using Bayesian Estimation." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804882/.
Full textAbstract:
This study presents a set of data analysis approaches for single subject designs (SSDs). The primary purpose is to establish a series of statistical models to supplement visual analysis in single subject research using Bayesian estimation. Linear modeling approach has been used to study level and trend changes. I propose an alternate approach that treats the phase change-point between the baseline and intervention conditions as an unknown parameter. Similar to some existing approaches, the models take into account changes in slopes and intercepts in the presence of serial dependency. The Bayesian procedure used to estimate the parameters and analyze the data is described. Researchers use a variety of statistical analysis methods to analyze different single subject research designs. This dissertation presents a series of statistical models to model data from various conditions: the baseline phase, A-B design, A-B-A-B design, multiple baseline design, alternating treatments design, and changing criterion design. The change-point evaluation method can provide additional confirmation of causal effect of the treatment on target behavior. Software codes are provided as supplemental materials in the appendices. The applicability for the analyses is demonstrated using five examples from the SSD literature.
47
Jamasebi, Reza. "COMPUTATIONAL PHENOTYPE DERIVED FROM PHYSIOLOGICAL TIME SERIES: APPLICATION TO SLEEP DATA ANALYSIS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1220467153.
Full text
48
Jamasebi, Reza. "Computational phenotype derived from physiological time series application to sleep data analysis /." online version, 2009. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=case1220467153.
Full text
49
Addo, Peter Martey. "Modern approaches for nonlinear data analysis of economic and financial time series." Thesis, Paris 1, 2014. http://www.theses.fr/2014PA010033/document.
Full textAbstract:
L’axe principal de la thèse est centré sur des approches non-linéaires modernes d’analyse des données économiques et financières, avec une attention particulière sur les cycles économiques et les crises financières. Un consensus dans la littérature statistique et financière s’est établie autour du fait que les variables économiques ont un comportement non-linéaire au cours des différentes phases du cycle économique. En tant que tel, les approches/modèles non-linéaires sont requis pour saisir les caractéristiques du mécanisme de génération des données intrinsèquement asymétriques, que les modèles linéaires sont incapables de reproduire.À cet égard, la thèse propose une nouvelle approche interdisciplinaire et ouverte à l’analyse des systèmes économiques et financiers. La thèse présente des approches robustes aux valeurs extrêmes et à la non-stationnarité, applicables à la fois pour des petits et de grands échantillons, aussi bien pour des séries temporelles économiques que financières. La thèse fournit des procédures dites étape par étape dans l’analyse des indicateurs économiques et financiers en intégrant des concepts basés sur la méthode de substitution de données, des ondelettes, espace incorporation de phase, la m´méthode retard vecteur variance (DVV) et des récurrences parcelles. La thèse met aussi en avant des méthodes transparentes d’identification, de datation des points de retournement et de l´évaluation des impacts des crises économiques et financières. En particulier, la thèse fournit également une procédure pour anticiper les crises futures et ses conséquences.L’étude montre que l’intégration de ces techniques dans l’apprentissage de la structure et des interactions au sein et entre les variables économiques et financières sera très utile dans l’élaboration de politiques de crises, car elle facilite le choix des méthodes de traitement appropriées, suggérées par les données.En outre, une nouvelle procédure pour tester la linéarité et la racine unitaire dans un cadre non-linéaire est proposé par l’introduction d’un nouveau modèle – le modèle MT-STAR – qui a des propriétés similaires au modèle ESTAR mais réduit les effets des problèmes d’identification et peut aussi représenter l’asymétrie dans le mécanisme d’ajustement vers l’équilibre. Les distributions asymptotiques du test de racine unitaire proposées sont non-standards et sont calculées. La puissance du test est évaluée par simulation et quelques illustrations empiriques sur les taux de change réel montrent son efficacité. Enfin, la thèse développe des modèles multi-variés Self-Exciting Threshold Autoregressive avec des variables exogènes (MSETARX) et présente une méthode d’estimation paramétrique. La modélisation des modèles MSETARX et des problèmes engendrés par son estimation sont brièvement examinésThis thesis centers on introducing modern non-linear approaches for data analysis in economics and finance with special attention on business cycles and financial crisis. It is now well stated in the statistical and economic literature that major economic variables display non-linear behaviour over the different phases of the business cycle. As such, nonlinear approaches/models are required to capture the features of the data generating mechanism of inherently asymmetric realizations, since linear models are incapable of generating such behavior.In this respect, the thesis provides an interdisciplinary and open-minded approach to analyzing economic and financial systems in a novel way. The thesis presents approaches that are robust to extreme values, non-stationarity, applicable to both short and long data length, transparent and adaptive to any financial/economic time series. The thesis provides step-by-step procedures in analyzing economic/financial indicators by incorporating concepts based on surrogate data method, wavelets, phase space embedding, ’delay vector variance’ (DVV) method and recurrence plots. The thesis also centers on transparent ways of identifying, dating turning points, evaluating impact of economic and financial crisis. In particular, the thesis also provides a procedure on how to anticipate future crisis and the possible impact of such crisis. The thesis shows that the incorporation of these techniques in learning the structure and interactions within and between economic and financial variables will be very useful in policy-making, since it facilitates the selection of appropriate processing methods, suggested by the data itself.In addition, a novel procedure to test for linearity and unit root in a nonlinear framework is proposed by introducing a new model – the MT-STAR model – which has similar properties of the ESTAR model but reduces the effects of the identification problem and can also account for asymmetry in the adjustment mechanism towards equilibrium. The asymptotic distributions of the proposed unit root test is non-standard and is derived.The power of the test is evaluated through a simulation study and some empirical illustrations on real exchange rates show its accuracy. Finally, the thesis defines a multivariate Self–Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The modeling procedure for the MSETARX models and problems of estimation are briefly considered
50
Addo, Martey Peter <1986>. "Modern approaches for nonlinear data analysis of economic and financial time series." Doctoral thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/5586.
Full textAbstract:
This thesis centers on introducing modern non-linear approaches for data analysis in economics and finance with special attention on business cycles and financial crisis. It is now well stated in the statistical and economic literature that major economic variables
display non-linear behaviour over the different phases of the business cycle. As such, nonlinear approaches/models are required to capture the features of the data generating mechanism of inherently asymmetric realizations, since linear models are incapable of
generating such behavior.
In this respect, the thesis provides an interdisciplinary and open-minded approach to analyzing economic and financial systems in a novel way. The thesis presents approaches that are robust to extreme values, non-stationarity, applicable to both short and long data length, transparent and adaptive to any financial/economic time series. The thesis provides step-by-step procedures in analyzing economic/financial indicators by incor-
porating concepts based on surrogate data method, wavelets, phase space embedding, ’delay vector variance’ (DVV) method and recurrence plots. The thesis also centers on transparent ways of identifying, dating turning points, evaluating impact of economic
and financial crisis. In particular, the thesis also provides a procedure on how to an-ticipate future crisis and the possible impact of such crisis. The thesis shows that the incorporation of these techniques in learning the structure and interactions within and
between economic and financial variables will be very useful in policy-making, since it facilitates the selection of appropriate processing methods, suggested by the data itself.
In addition, a novel procedure to test for linearity and unit root in a nonlinear framework is proposed by introducing a new model – the MT-STAR model – which has similar properties of the ESTAR model but reduces the effects of the identification problem and
can also account for asymmetry in the adjustment mechanism towards equilibrium. The asymptotic distributions of the proposed unit root test is non-standard and is derived.
The power of the test is evaluated through a simulation study and some empirical illustrations on real exchange rates show its accuracy. Finally, the thesis defines a
multivariate Self–Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The modeling procedure
for the MSETARX models and problems of estimation are briefly considered.