Visual Analysis of Periodic Time Series Data

Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. in 4 koe 2020 Oktober 14. Wien, is israinhbnbegutachtet: haben Dissertation Diese Filzmoser Peter Dr.techn. Dipl.-Ing. Univ.Prof. Zweitbetreuung: Miksch Silvia Dr.rer.soc.oec. Mag.rer.soc.oec. Univ.Prof. Betreuung: Wien Universität Technischen der Informatik für Fakultät der an ohrae muainudAusreißererkennung und Imputation Vorhersage, iulAayisfrdeModellauswahl, die für Analytics Visual eidshnZeitreihen periodischen otrdrTcnshnWissenschaften Technischen der Doktor -00Wien A-1040 iuleAayevon Analyse Visuelle u ragn e kdmshnGrades akademischen des Erlangung zur il-n.Mru öl BSc Bögl, Markus Dipl.-Ing. alpaz13 Karlsplatz arklumr00625252 Matrikelnummer ehiceUiesttWien Universität Technische DISSERTATION osMaciejewski Ross igrih von eingereicht e.+43-1-58801-0 Tel. www.tuwien.at aaa Turkay Cagatay aksBögl Markus Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. dio:Ui.rf a.e.o.e.D.e.o.e.Sli Miksch Silvia Dr.rer.soc.oec. Mag.rer.soc.oec. Univ.Prof. Advisor: ina 14 Vienna, h israinhsbe eiwdby: reviewed been has dissertation The eodavsr nvPo.Dp.Ig rtcn ee Filzmoser Peter Dr.techn. Dipl.-Ing. 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Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. ü igtudFrederik und Birgit Für ü en Familie meine Für Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Visualization inr lxne id hitn idrr n aksWge;m nentoa collaborators international my Wagner; Markus and Niederer, Christina Rind, Alexander Aigner, Paolo Amor-Amorós, Albert Alsallakh, Bilal Schetinger, Victor Roger Gschwandtner, Piccolotto, Nikolaus Theresia Filipov, Leite, Velitchko A. Ceneda, Davide Bors, Christian Arleo, Alessio in lecture His Filzmoser. Peter advisor, second my thank to want CAT,fne yteAsra eea iityo cec,Rsac,adEooyi the in Economy and Research, Science, of Ministry Federal Austrian the by funded (CVAST), era h tdn.Ia seilygaeu oteognzr o hsaaigopruiyto opportunity amazing this for organizers the to grateful especially am I student. PhD a as year PhD. my pursuing while analytics visual yrsac a nnilyspotdb h etefrVsa nltc cec n Technology and Science Analytics Visual for Centre the by supported financially was research My forget never will me. doubts, I for my did overcome concepts. you musical me everything checking pastries, helping and dinner, babysitting, translating, vegetables, (lunch, made proofreading, gestures food and questions, tiny answering fruits bringing/preparing many fresh So possible: bringing dissertation cake), program. and this PhD of my extended phase my during final from me people the supported magnificent and several for helped gratitude who great family my express project. to research want international I Finally, an for my cooperation build in helped resulted Jörn eventually greatly notably, and papers—most This network and Andrienko. research books Gennady the and from Natalia knew Maciejewski, I Ross Kohlhammer, researchers famous those first my person in during meet 2013, in in participate UK, University, to Middlesex opportunity at the School had Summer the I Analytics as Visual feedback. well last valuable as great the and events the efforts, other for time, and community colloquium, their analytics for doctoral visual reviewers conferences, and workshops, visualization talks, the the in at people time all thank to Schumann. want Heidrun I and Lastly, Rostock, Tominski, Universität Christian from Eichner, those Christian and Luboschik, Kohlhammer; Martin Röhlig, Jörn and Martin Bernard Jürgen Wolfgang Darmstadt, Pölten, TU from St. Wien, FH TU the at from Department collaborators Statistics my Computational Mühlmann; the Christoph from and collaborators Nordhausen my Klaus to grateful also am I Lammarsch. Tim and Wien: Federico, TU at group research CVAST the from colleagues former and current all thank to and want analysis I data exploratory statistics, in interest my continue to me allowed Peter and Silvia I addition, In dissertation. encouragement, my support, finish eventually her for to Miksch, push Silvia final advisor, first and my attitude, to positive you enthusiasm, thank special a say to want I tre yjunyt iulaayis n h nedsilnr oprto between cooperation interdisciplinary the and analytics, visual to journey my started Acknowledgments xlrtr aaAayi and Analysis Data Exploratory ix Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. #283;teFFb BidSuc eaaini ieadSae (#P31881-N32). Space” and Time in Separation Methods” Source Analytics “Blind Visual by with FWF Hypotheses the Modeling (#P22883); “HypoVis: by FWF the (#I2850-N31); rcdr DC)“ISC:Vsa emnainadLbln fMliait ieSeries” Time Multivariate Agency of Lead Labeling the and by Segmentation Doctoral (FWF) Visual “VISSECT: the Fund (DACH) by Science Procedure Wien Austrian TU the Informatics; the Environmental (#822746); for initiative College Excellence of Centre Bassi Laura exceptional Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. alrzs nesüz id auemgih e nazdevsel nls e eteh,bietet Zeitreihe, der Modellaus- Analyse gesamte visuelle der die Ansatz dem der mit ermöglicht vor, Dazu Analytics-Ansatz wird. Visual unterstützt wahlprozess einen wir stellen Schritt ersten Als untersuchen und identifizieren Muster darunterliegenden die um explorieren, zu Analytics Visual zu Ausreißer und beurteilen zu Werte fehlende geschätzte interpretieren, zu erkannt besser Vorhersagen Periodizität die solange Vorteil, von Eigenschaft diese eben ist Zeitreihen von Analyse cäzne o elne aepntn uvresr n is uvroltnie.Danach vervollständigen. zu diese und verbessern zu Datenpunkten, fehlenden von Schätzungen für ( Darstellungsform Zeitreihen spezielle eine periodische wir verwenden Schritt in nächsten die Im Modellparameter wir Modellauswahlprozess. und den untersuchen Modelle Weiters gewählten Daten. der Vorhersagemöglichkeiten die genaue der die für Zusammenführung für Modelle sowie der Modellparameter Angemessenheit und bezüglich Modelle Diagnose der Auswahl die für Orientierungshilfe eine beeinflusst. positiv Zeitreihen die von Berechnungen, Analyse statistischen herausfordernde mit und mit Interaktionstechniken gemeinsam mit Zeitreihen Wahrnehmung, periodische wie menschlicher auf bzw. Betrachtungswinkel können werden visueller unterstützt neuer Verflechtung bestmöglich wird Aufgaben die es diesen und in untersucht Nutzer*innen all Analytics-Methoden wie Für Visual Ausreißern. ermittelt, von von Anwendbarkeit Erkennen die das wird sowie Schritte Werten diese fehlenden Modelle, von der Vorhersagequalität Ersetzen der und Berechnen Überprüfen das das Parametrisierung, Zeitreihenmodelle, der passender in möglichst Unterstützung Auswahl die die Zeitreihen, und von Zeitreihen Explorieren periodische das Speziellen umfassen Zeitreihen- im betreffen der Herausforderungen in Diese Herausforderungen betrachtet. verschiedenste analyse werden Dissertation vorliegenden der In von mithilfe Daten können. die zu oder Möglichkeit erkennen eine zu besteht Darstellungen identifizieren, visueller zu geeigneter solche mittels Um sie sein. gegeben versteckt darin, Daten selbst der Daten Zusammenhang den dem in aus oder direkt können Muster periodischen auszuwählen, Die Zeitreihenmodelle erkennen. passende helfen, sie kann Dann wird. die abgebildet Für richtig Muster. wiederkehrende und periodisch durch Datensätzen vielen in sich Eigen- zeigt strukturelle Zeit Diese gemessenen der ist. der schaft bedingt Struktur Kalenderstrukturen natürliche dahinterliegende die durch treffen. durch oder die zu Phänomene Zeitreihen, Entscheidungen von darauf, aufgezeichnet, Eigenschaft basierend eine wird ist und Daten Periodizität Na- analysieren von zu Wirtschaft, Art festzuhalten, in Diese Messungen mehr. es um dergleichen sei und präsent, Medizin Forschungsbereichen turwissenschaften, unterschiedlichen in sind Zeitreihen Zyklengraph bzw. yl plot cycle ,u i muain a eß,depassenden die heißt, das Imputation, die um ), Kurzfassung xi Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. et oi a dnizee o urienvresr z.es ermöglicht. erst bzw. verbessert Ausreißern von Identifizieren das sowie Werte nedaki e näz uc uzrinnznre n rnlc ecreeeFunktiona- beschriebene gründlich und Nutzer*innenszenarien durch und Ansätze Nutzen sowie der Anwendbarkeit verwendet Designprozesse nutzer*innenzentrierte iterative wurden Ansätze o muainnudAseßr.DeFrcugegbis egn asdeevselnDarstel- visuellen diese dass zeigen, Forschungsergebnisse Die Ausreißern. und Imputationen von ein Analytics-Ansätzen Visual bei erlaubt Darstellung visuellen der in Periodizität vorhandener u i ae itn ai iddeAsalagmsee oel z.Mdlprmtr das Modellparameter, fehlender bzw. Imputation Modelle die angemessener Modellauswahlprozess, Auswahl im Vorhersagemöglichkeiten Sichtweisen die der andere wird Miteinbeziehen Zeitreihen Damit von bieten. Analyse Daten die für die Zeit auf der Struktur Berücksichtigung periodischen gleichzeitiger speziellen bei der Datenabstraktion passenden der mit gemeinsam lungsformen Vorhersagen, von Zeitreihenmodellen, angewandten von Nachvollziehen und Verstehen besseres von Miteinbeziehen Das diskutiert. Forschungsmöglichkeiten sowie weitere beschrieben und Ansätze Fragestellungen dieser offene Auswirkungen die werden Abschließend bestätigt. litätstests vorgeschlagenen dieser jeden in Für Ausreißern kann. von werden Erkennen verwendet das Zeitreihen für periodischen und multivariaten konstruiert mithilfe Zeitreihen Zyklengraph multivariate dieser für wie Abstraktion Datenabstraktion, dieser neuartigen einer Verwendung unter wir zeigen Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. efis rps iulaayisapoc o uprigtewoepoeso selecting of process whole the supporting for approach analytics visual a propose first We epeetanvlasrcinmto oueaccepo ersnainfrmliait time multivariate for representation plot cycle a use to method abstraction novel a present we edsusdteipiain fsc ehd n ocue ihoe hlegsi these in challenges open with concluded and methods such of implications the discussed we iesre aaaeesnili ayfils ieeoois aua cecs n medicine, and sciences, natural economics, like fields, many in essential are data series Time eisa eli re ouei o ule eeto nproi iesre.Frec fthe of each For series. time periodic in detection outlier for it use to order in well as series onm e.Mauigadrcrigteedt lo st ouet nlz,admake and analyze, document, to us allow data these recording and Measuring few. a name to tutr ftm,alw o h nlsso iesre rmdffrn esetvsadprovides and perspectives different from series time of periodic analysis the the for considering allows when time, abstraction, The of and structure representation outliers. visual and adequate imputations, that predictions, approaches indicate models, analytics results applied visual the the comprehending into better periodicity for visualizing allows on focus additional Furthermore, Integrating walk-throughs. topics. illustrated thoroughly and utility scenarios the usage showed in we process; approaches design the user-centered of iterative an employed we solutions, proposed Thereafter, series. time periodic cycle in a employ values we missing Next, of imputation process. the selection support model to the representation into integrate plot model to the how investigate the of then guiding capabilities We while prediction diagnostics. the series model time and of exploration parametrization, visual selection, the model allowing models, series time appropriate analyzing in user the series. with fosters time together computations periodic visualization statistical tasks and using these techniques, series in interaction time users perception, periodic support user on can perspectives analytics new visual intertwining how investigate how we and steps, detecting these and all the values, For supporting missing models, outliers. imputing series series, performance, time time prediction appropriate periodic the for examining selecting analysis parametrization, series, series time time the of exploring stages with different starting the consider we dissertation, exploration, this the analysis.In for Visualization visual allow using and itself. patterns easily data underlying patterns such the such of in identify investigation hidden to and or perception identification, context human for the way to one due These is obvious detection. be outlier can and modeled a patterns imputation, and In prediction, periodic correctly selection, reoccurrences. identified model periodic if like by beneficial, tasks series mostly for time adequately, are these properties One of these many analysis, structure. in series calendar which time time underlying periodicity, of is the property areas or structural all measured the across phenomena finds series natural time the in either structures from natural stems most the of One decisions. Abstract xiii Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. aus n dniyn outliers. identifying and values, osblte o dniyn dqaetm eismdl,spotn h muaino missing of imputation the supporting models, series time adequate identifying for possibilities Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Introduction 1 Exposition I Contents Abstract Kurzfassung nertn rdcin nTm eisMdlSlcin53 Selection Model Series 29 Time in Predictions Integrating 3 Analysis Series Time in Selection Model for Analytics Visual 2 Development II . sg cnro...... 57 54 . . . . 54 ...... 46 ...... 50 ...... 31 37 ...... 30 ...... 32 ...... 38 ...... Scenario ...... Usage ...... Approach . . . . . Analytics 3.3 . . Visual ...... 3.2 . . . . Introduction ...... 25 ...... 3.1 . . 24 ...... 14 ...... 19 Work ...... Future . and ...... Conclusion ...... 10 . . . . Analysis . . . 2.7 Series Evaluation . . . . . Time . . . in . . . . . Selection . . . Model . . 2.6 . . . for . . . VA ...... Analysis 2.5 Requirements ...... Characterization . 2.4 ...... Problem 5 ...... Work 2.3 Related ...... 4 ...... 2.2 . . . . Introduction ...... 2.1 ...... Contributions . and . . . . . Aim ...... Questions 1.7 . Research ...... Analysis . Series 1.6 . . . Time ...... 1.5 . Methodology ...... Analytics 1.4 . Visual . Visualization and . Analysis 1.3 Data Analysis Problem and 1.2 Motivation 1.1 Contents xiii 27 xv xv xi 3 1 Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Bibliography Tables of List Figures of List h utvraeCcePlot Cycle Multivariate The 5 61 Series Time Seasonal Univariate in Imputation Guided Statistically and Visually 4 Conclusion 6 Coda and Recapitulation III . ulctos...... 104 98 . . . . 93 ...... 95 . . . 98 ...... Publications . . Opportunities . Future . . and . Challenges 6.5 . . Open . . . . . 6.4 . Conclusion . . . Revisited Questions 6.3 . Research . 6.2 Summary 6.1 . eae ok...... 62 65 62 ...... 63 ...... 58 ...... 58 ...... Conclusion . . . . . and Discussion ...... Approach Imputation 4.4 . . . . Time-Series ...... Work 4.3 Related ...... 4.2 . . Introduction . . . . 4.1 ...... Conclusion and . Discussion . Work 3.5 Related 3.4 . ocuin...... 85 81 . . . 79 ...... 70 ...... 69 ...... 68 . . . 86 ...... 75 ...... 72 ...... Scenario . . . . Usage . . . for . . . . Material . . . . Supplementary ...... Appendix: ...... 5.9 . . Conclusion ...... 5.8 . . . . Discussion ...... Scenario 5.7 . Usage Environment . . . Exploration . Interactive . . . the of 5.6 . . Measures . Features Distance . for . . Requirements and . . . 5.5 Abstraction Task ...... 5.4 . . Background . . . Work 5.3 Related . . 5.2 Introduction 5.1 109 107 111 89 67 91 Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Exposition atI Part 1 Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. 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The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. . i n Contributions and Aim 1.7 ete aetese otescn anpart, main second the to step the take then We okbfr umrzn h otiuin oehrwt noeve ftermiigstructure remaining the of overview an with together contributions the summarizing before work the In dissertation. a of structure the in required is it as purpose, specific a has which h neto of intention The analysis, series time statistical in challenges of understanding an provide to is work this of aim The aigtd eaopoe,cnrse,adopsdutlte rnfr into transform they until opposed and contrasted, metamorphosed, variegated, nltc fproi iesre aai hpe .Atrmtvtn h oi ngnrland general in topic the motivating After 1. Chapter in data series time periodic of analytics nCatr3 eetn h oe eeto rcs n rps h nerto fprediction of integration the propose and process selection model the extend we 3, cycles. 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The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. 1. 26 Introduction ecniu ihitouigvsal n ttsial uddiptto fmsigvle in values missing of imputation guided statistically and visually introducing with continue We eeatpbiain oehrwt noeve ftecnrbtost hs aeso h author the of of list papers a these to with contributions end dissertation, research the We this the of of overview answer directions. an research thesis, with future this together and publications of relevant challenges contributions open main propose the and 6 questions, Chapter in discuss the and is conclude, dissertation this of part main and the explore cycles. 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[ Chapter al. in series time seasonal univariate task. selection model insights the additional for give model to the series and of time parsimony prediction input and compute the adequateness to of the context model into the the in apply visually and prediction selection the model provide this for of part content diagnostic [ The model al. process. the et selection Bögl model in the published improve was to paper models series time of capabilities BFG + 15 .Tegnrlie fti otiuini opoieitiiegiac o h akof task the for guidance intuitive provide to is contribution this of idea general The ]. aksBögl Markus . BAF + BFG 15 .I diint h rvoscnrbto,w extend we contribution, previous the to addition In ]. + 17 eaiuainadcoda and recapitulation .I hscnrbto,w aeteimputation the take we contribution, this In ]. nwihw summarize, we which in , Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Development atII Part 27 Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. 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The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. 32 2. iulAayisfrMdlSlcini ieSre Analysis Series Time in Selection Model for Analytics Visual nti eto epoietecaatrzto ftedmi rbe [ selection. problem model domain the the for of necessary characterization tasks the provide we section this In Characterization Problem 2.3 motivation our ground and detail following more the in problem in target information our further. background specify necessary and characterize the to give section we solutions. their findings, and these tasks by the Motivated in differ but data, time-oriented to methods VA apply TimeSearcher series example approaches time for as statistical VA, in in work selection related distantly model [ only of is there problem knowledge, target our to specific analysis, very the have we Because directly and browse to not history but a settings provides previous them. also loading compare It for allows only. which input models, form-based computed using outliers for call of X-12-ARIMA editing the manual seasonal for interactive the parameters do of the to results user the the and enable series to time [ as the well explore as to adjustment is series approach time their of focus The software. the for interface the is well. solution repeated notable [BJ70] the One methodology support Box-Jenkins not arrange iterative do and the they models in summarize, of steps To bunch separate whole them. the a compare of compute execution to or hand by models of visualizations set in a them on decide either to necessary process. the series about time details of for selection 2.3.1 model Section for See process iterative [BJR08]. 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The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. 56 3. nertn rdcin nTm eisMdlSelection Model Series Time in Predictions Integrating esoe ieetwy fhglgtn h ieec ewe culadpeitdvle in values predicted and actual between difference the highlighting of ways different showed We epooet ipa h ieec sn ulznGah[ Graph Qualizon using difference the display to propose we from values parameter of progress the highlight to scale color diverging a use authors the where vertical save to enables band, color accuracy an such Using deviation. standard the to respect with 1-)frtredffrn oes ulznGah r xesoso oio rps[ Graphs Horizon of extensions are Graphs Qualizon models. different three for (1a-c) h srcnslc h oe addtsuigtemdlslcinhsoyi iue312.In 3.1.2e. 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Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. h rgnlvrini vial at available is version original The [ in published was chapter this of content The cec n ehooy AT–Pses hcg,I,UA coe 53,2015 25-30, October USA, IL, Chicago, Posters, – VAST Technology, and Science EE 2015. IEEE, in values missing In of imputation series. guided time statistically seasonal and Visually univariate Lammarsch. 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The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. 66 4. iulyadSaitclyGie muaini nvraeSaoa ieSeries Time Seasonal Univariate in Imputation Guided Statistically and Visually sli u nteitouto,msigvle r i su ntm eisdt rmra world real from data series time in issue big a are values missing introduction, the in out laid As in applicable not is approach our that is limitation Another outlier. an can is approach really value the the Furthermore, whether imputation. the improve to order in excluded be then may which hr r eea osblte oetn u prah o utvraetm eisapossible a series time multivariate For approach. our extend to possibilities several are There au.Idctn h iepit novdi h muainmyhl dniyn upcosvalues, suspicious identifying help may imputation the in involved points time the Indicating value. oankoldeada piie iulrpeetto o esnltm series. time seasonal for representation visual optimized an and knowledge domain of use make for and views approach separate our instance, extend for components. to component, seasonal is each and for idea trend views One several with trend. strong series very time judge a decomposed to has method series imputation time the the of case outcome the compare and value suspicious a impute to used missing be a for estimate good a provide not structure. will cyclic outliers the on representing based visually extension imputations to this that techniques For is appropriate limitation values. more One imputed about the think improve to to needs used one be can variables the between correlation values, these about confidence the raise a values. values, enables unsuitable imputed This adjust the and of representations. adequacy box-plot the in values of about imputed judgement information the better detailed of imputation provides variation the and and views of uncertainty and both outcome the linking into The Using directly arrangements. embedded side. visually different by two is side these methods arrangement, with cyclic of a track series in keeping time view helps the a displaying brushing with for coordinated view axis a time provide linear we a interface, visual interactive an with methods plctos u prahepnso h osblte fiptto ehd yincorporating by methods imputation of possibilities the on expands approach Our applications. 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The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. we’i sdt raetetc ak ntehrzna xs h aapit r hw for shown are points data The axis. horizontal the granularity on the marks of tick granule the each left) create 5.1, to Figure used (see plot is line ‘week’ conventional the In ‘week’. and ‘day’ hnvrw att xlctyrfrt h rgnltcnqe[ technique original the to refer explicitly to want we whenever one called is day specific each while ilrfrto refer will au o hsdyo eki niae yahrzna line. horizontal a by indicated is week of day this for value aus n esrmn o ahJnayo hs er.Lkws,temaue o month for measures the Likewise, years. 8 these of January each for measurement one values: C C ahgaueo ahgauaiy olwn h omlodro ie nteccepo (see plot cycle the In time. of order normal the following granularity, each of granule each o h eann ato h ae eseiyvralsadst o h xlntos We explanations. the for sets and variables specify we paper the of part remaining the For Specification Variable 5.3.2 term the use we plot following cycle the original Cleveland’s In Yet only. today. data used series and time known [ univariate commonly Cleveland represents by is work it later like the subseries, in the mean, for the representing line horizontal the [ pattern” plots [ Terpenning seasonal line seasonal and the “as make Cleveland shows shown “To that are is: plot trends plot a individual cycle are within the the week embedded and of the discernable”, objective of visually days the components other that trend All state and Hence, al. weeks. et four etc.). 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Silvia and Federico, Paolo Bögl, Markus Gschwandtner, Theresia hita os aksBg,Teei shadnr n ivaMkc.Visual Miksch. Silvia and Gschwandtner, Theresia Bögl, Markus Bors, Christian ae 0–1.Srne,2013 Springer, 400–419. pages , 21:3–4,2016 22(1):539–548, , ae 35.AM 2017 ACM, 53–57. pages , ae 13.TeErgahc soito,2016 Association, Eurographics The 31–35. pages , ua-optrItrcinadKoldeDiscovery Knowledge and Interaction Human-Computer ae 6–7.IE,2014 IEEE, 269–270. pages , ae 14.IE,2015 IEEE, 41–48. pages , rceig fte1t International 10th the of Proceedings rceig fte5hInternational 5th the of Proceedings EETascin nVisualization on Transactions IEEE oue74 of 7947 volume , rceig of Proceedings etr Notes Lecture . Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. 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[Bec00] [BG05] [BFG [Ber83] [BDB [BD10] [BD02] [BK11] [BJW00] [BJ70] [BFG [BD91] [BJR08] + + + 15] 17] 16] oeatn n Control and Forecasting iigadKoldeDsoeyHandbook Discovery Knowledge and Mining aaae,Dt iigadTmoa Reasoning Temporal and Mining Data Databases, guided statistically and Visually Lammarsch. Tim and Rind, Alexander Aigner, Control 2015 25-30, October USA, IL, Chicago, h uorpisAscain 2016. Association, 2016 Eurographics The 6-7, June Netherlands, The Groningen, 2016, EuroVA@EuroVis Example pigrSre nSaitc.Srne,NwYr,N,UA n dto,1991. edition, 2nd USA, NY, York, New Springer, Davis. Statistics. A. in Series Richard Springer and Brockwell J. Peter øe igadadMrtKulahci. Murat and Bisgaard Søren Wang. X. Sean and Jajodia, G. Sushil Bettini, Claudio Reinsel. C. Gregory and Jenkins, M. Gwilym Box, P. E. George Jenkins. M. Gwilym and Box P. E. George editors, Rokach, Lior and Maimon Oded In detection. Outlier Ben-Gal. Irad Multivariate revisited: plot 2017. abstraction. Cycle 36(3):227–238, distance-based a Rind. using Alexander detection outlier and Miksch, A. Roger Silvia Lammarsch, Tim Leite, Gschwandtner, Theresia Filzmoser, Peter Bögl, Markus Posters, – VAST Technology, and Science Analytics In Visual series. on Conference time IEEE seasonal the univariate in values missing of imputation Wolfgang Miksch, Silvia Gschwandtner, Theresia Filzmoser, Peter Bögl, Markus graphique" "Sémiologie french 1967. in in published originally Berg, J. William by translated Bertin. Jacques 2000. MA, Reading, Beck. Kent series. time multivariate of In segmentation Vögele, Visual-interactive Anna Kohlhammer. Röhlig, Jörn Martin and Bögl, Markus Dobermann, Eduard Bernard, Jürgen 2010. Heidelberg, Berlin Springer, Health. and Biology for Dobson. J. Annette and Barnett G. 2002. Adrian edition, 2nd USA, NY, York, New Springer, Statistics. in Texts Springer Davis. A. Richard and Brockwell J. Peter rceig fte7hItrainlErVsWrso nVsa Analytics, Visual on Workshop EuroVis International 7th the of Proceedings odnDy a rnic,C,UA 1970. USA, CA, Francisco, San Holden-Day, . onWly&Sn,Hbkn S,2011. USA, Hoboken, Sons, & Wiley John . xrm rgamn xlie:EbaeChange Embrace Explained: Programming Extreme eilg fGraphics of Semiology onWly&Sn,Hbkn S,4heiin 2008. edition, 4th USA, Hoboken, Sons, & Wiley John . ieSre nlssadFrcsigby Forecasting and Analysis Series Time h nvriyo icni rs,1983. Press, Wisconsin of University The . nrdcint ieSre n Forecasting and Series Time to Introduction ae 8–9.IE,2015. IEEE, 189–190. pages , nlsn esnlHat Data Health Seasonal Analysing ieSre nlss oeatn and Forecasting Analysis: Series Time ae 3–4.Srne S 2005. US, Springer 131–146. pages , ieSre:Ter n Methods and Theory Series: Time pigr 2000. Springer, . optrGahc Forum Graphics Computer ieGauaiisin Granularities Time ieSre Analysis: Series Time Addison-Wesley, . rceig of Proceedings ae 31–35. pages , Statistics . Data 113 , . . Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. [CCH13] 114 [CB14] [BRG [BPC Lewis. Toby and Barnett Vic [BL98] [BP70] [BPS [CC08] [BW08] [BSH [Bro11] [BM13] + + + + 07] 10] 16] 12] 91)27–35 2013. 19(12):2376–2385, mrcnSaitclAssociation Statistical American rnatoso iulzto n optrGraphics Computer and Visualization on Transactions isn aaExploration Data Missing iulitrciepercsigo iesre aa nAdesKre n Stefan and Kerren Andreas In data. series time of preprocessing Visual-interactive Graphics R oue8,pgs3–8 ikpn nvriyEetoi rs,2012. Press, Electronic University Linköping 39–48. pages 81, volume nentoa ofrneo nomto Visualisation Information on Conference International ate rhe n aaaMnnr ut-ee yooyo btatvi- abstract of typology multi-level A tasks. sualization Munzner. Tamara and Brehmer Matthew iou hn,Dan ok n ek Hofmann. Heike and Cook, Dianne Cheng, Xiaoyue Chan. Kung-Sik and Cryer D. Jonathan Publishing. Research Scientific time. of Publisher: 3 problem The Number: 2014. Balestra. 5(3):250–258, Luisella and Chong Choy Li aesthetics. & geometry – graphs Stacked Wattenberg. Martin and Byron Lee temporal 2016. of 22(1):559–568, patterns visualize to data. time in Folding evolution Curves: Time Grabowski, Tom Dragicevic. Madhyastha, Pierre Tara and Heulot, Nicolas Shi, Conglei Bach, Benjamin editor, 2011. Lovric, Science Miodrag Statistical In of series. clopedia Time Brockwell. J. Peter editors, Seipel, Kohlhammer. Jörn and May, Thorsten Goroll, Oliver Ruppert, Tobias Bernard, Jürgen In forecasting. series time for interface plot simultaneous river with forecasting A Similarity-based previews: Jank. Wolfgang Shneiderman, and Ben Aris, Shmueli, Aleks Galit Simeone, Adalberto Plaisant, Catherine Buono, Paolo effectiveness the on resolution block visualization. and pixel-based Thornton. demands of Ian task and Murray, of Tavi impact Jänicke, the Heike Evaluating Chen, Min Proctor, Karl Borgo, Rita autocorrelations models. residual series of time Distribution average moving Pierce. autoregressive-integrated A. in David and Box P. E. George pigrTxsi ttsis pigr e ok Y S,2deiin 2008. edition, 2nd USA, NY, York, New Springer, Statistics. in Texts Springer . 66:6–7,2010. 16(6):963–972, , rceig fSGA,ItrcieVsa nlsso Data of Analysis Visual Interactive SIGRAD, of Proceedings EETascin nVsaiainadCmue Graphics Computer and Visualization on Transactions IEEE EETascin nVsaiainadCmue Graphics Computer and Visualization on Transactions IEEE 03 akg eso 0.1-5. version package R 2013. , ulesi ttsia data statistical in Outliers EETascin nVsaiainadComputer and Visualization on Transactions IEEE ae 6110.Srne,Bri,Heidelberg, Berlin, Springer, 1601–1605. pages , 532:5912,1970. 65(332):1509–1526, , ieSre nlss ihApiain in Applications With Analysis. Series Time isnDtGI U for GUI A MissingDataGUI: ae 9–9.IE,2007. IEEE, 191–196. pages , 46:2515,2008. 14(6):1245–1252, , ie,3deiin 1998. edition, 3rd Wiley, . nentoa Ency- International rceig fthe of Proceedings oenEconomy Modern ora fthe of Journal IEEE , , , , Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. [Das13] [CT82] [Coh10] [Coh04] [CMS99] [CM09] [EHR [DGH06] [CTB Cleveland. S. William [Cle93] [CL13] [CG16] [CENC03] [Cle94] + + 12] 14] ely pe adeRvr J 2010. NJ, River, Saddle Upper Wesley, rnatoso iulzto n optrGraphics Computer and Visualization on Transactions //gretl.sourceforge.net ora fItlietIfrainSystems Information Intelligent of Journal adoko aaQuality Data of Handbook prices. electricity next-day predict to models ARIMA Systems nentoa ora fForecasting of Journal International VAST Technology, and Science Analytics Visual on Conference IEEE airCnrrs oai sioa rnic .Ngls n noi .Conejo. J. Antonio and Nogales, J. Francisco Espinola, Rosario Contreras, Javier n hitpe nrw.Tehmni h op e ietosfrvsa analytics. visual for directions new loop: the is Fiaux, human Patrick forecasting. The North, Andrews. Chris series Christopher Ramakrishnan, and Naren time Hossain, of Shahriar M. years Endert, Alex 25 Hyndman. J. Rob and Gooijer De G. Jan editor, Sadiq, Shazia In data. your in Monsters glitches: Data Dasu. Tamraparni 2012. In decomposition. event seasonal-trend abnormal for using analytics examination media and social detection Spatiotemporal S. David Ertl. Maciejewski, Thomas Ross and Jang, Yun Ebert, Bosch, Harald Thom, Dennis Chae, Junghoon seasonal for methods Graphical Terpenning. J. adjustment. Irma and Cleveland S. William Cohn. Mike 2004. USA, CA, City, Redwood Cohn. Mike 1999. Inc., think. Publishers to Kaufmann vision Morgan Using Shneiderman. Ben and In Mackinlay, D. Jock Card, K. Stuart 2009. USA, NY, York, New Springer, R! Use Metcalfe. V. Andrew and Cowpertwait S.P. Paul 1994. USA, Cleveland. S. William 1.9.12 Version Guide, User’s - Library Time-series Lucchetti. and Riccardo and Cottrell Allin optimize? processes visualization may What Golan. Amos and Chen Min ednsi nomto iulzto:uigvso othink to vision using visualization: information in Readings 83:0412,2003. 18(3):1014–1020, , srSoisApid o gl otaeDevelopment Software Agile For Applied: Stories User ora fteAeia ttsia Association Statistical American the of Journal uceigwt gl:Sfwr eeomn sn Scrum Using Development Software Agile: with Succeeding iulzn data Visualizing h lmnso rpigdata graphing of elements The ae 6–7.Srne elnHiebr,2013. Heidelberg, Berlin Springer 163–178. pages , ls iie nOt 2 2020). 12, Oct. on visited (last 23:4–7,2006. 22(3):443–473, , oatPes umt J S,1993. USA, NJ, Summit, Press, Hobart . 33:1–3,2014. 43(3):411–435, , rt:GURgeso,Econometric Regression, GNU Gretl: nrdcoyTm eiswt R with Series Time Introductory EETascin nPower on Transactions IEEE oatPes umt NJ, Summit, Press, Hobart . 21)21–62 2016. 22(12):2619–2632, , ac 2013. March , 737:26,1982. 77(377):52–62, , Addison-Wesley,. ae 579–581. pages , ae 143–152, pages , Proceedings Addison- . http: IEEE 115 . Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. [GAK [HA04] [GM13] [GH07] [Fun36] [FRGTA14] [Few08] [ERT [Fun37] [FGR05] [GBFM16] [GMPn99] [FHR 116 + + + 17] 14] 11] 1936. dacdVsa nefcs AVI Interfaces, Visual Advanced rica nelgneReview Intelligence Artificial RM oes kpigapoc essadtv ule approach. outlier additive versus approach Skipping models: ARIMA effects and events, plans, visualizing and Exploring CareCruiser: Seyfang. Andreas ls iie nOt 3 00,Jn 2008. June 2020), 13, Oct. on visited (last PacificVis Econometrics business_intelligence/time_on_the_horizon.pdf gai ízBac,adFbieRsi h tt fteati nertn machine integrating in art the Nabney, of analytics. state Ian visual The into Wong, Rossi. learning William Fabrice and Turkay, Blanco, Cagatay Díaz Ribarsky, Ignacio William Endert, Alex itraJ og n i utn uvyo ule eeto methodologies. detection outlier of survey A Austin. Jim and Hodge J. Victoria in observations Missing Peña. Daniel and Maravall, Agustín Gómez, Víctor 2013. Analysis, Economic of Tech- Bureau adjustment. report, seasonal nical to approach multivariate A Greenaway-McGrevy. Ryan Models level/Hierarchical Hill. Jennifer and Gelman Andrew Graphics Computer study. and user Visualization comparative on A Visual uncertainty: Miksch. temporal Silvia of and encodings Federico, Paolo Bögl, Markus Gschwandtner, Theresia In interactively. and Miksch, Silvia Kaiser, Katharina Aigner, Wolfgang Gschwandtner, Theresia representation data. graphical statistical the of of development Historical Funkhouser. Gray Howard graph. century tenth a on note A Funkhouser. Gray Howard of Identification outliers. multivariate Thomas-Agnan. local Christine and Ruiz-Gazen, Anne Filzmoser, Peter qualitative In with visualization abstractions. time-series Space-efficient Silvia Graphs: and Qualizon Aigner, Wolfgang Miksch. Rind, Alexander Hoffmann, Stephan Intelli- Federico, Paolo Business Visual outlier Multivariate 2005. Reimann. geochemistry. Clemens exploration in and detection Garrett, G. Robert horizon. Filzmoser, Peter the on Time Newsletter, gence Few. Stephen ae 35,2011. 43–50, pages , 82:4–6,1999. 88(2):341–363, , rceig fte1t nentoa okn ofrneon Conference Working International 12th the of Proceedings rceig fte4hIE aicVsaiainSymposium, Visualization Pacific IEEE 4th the of Proceedings Osiris http://www.perceptualedge.com/articles/visual_ :6–0,1937. 3:269–404, , abig nvriyPes e ok S,2007. USA, York, New Press, University Cambridge . ttsia Papers Statistical 22:516 2004. 22(2):85–126, , optrGahc Forum Graphics Computer ae 7–8.AM 2014. ACM, 273–280. pages , aaAayi sn ersinadMulti- and Regression Using Analysis Data optr Geosciences & Computers 21:3–4,2016. 22(1):539–548, , 51:94,2014. 55(1):29–47, , 68:5–8,2017. 36(8):458–486, , ie a.2,2016, 20, Mar. cited , EETransactions IEEE Osiris 31(5):579–587, , 1:260–262, , ora of Journal Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. [HCL05] [HK07] [HKPC19] [HK10] [HB03] [HN98] [HJM [HA18] [HR07] [HKF16] [Ham94] [HKB11] [HJS + + 09] 11] mrcnStatistician American 2009 VAST ’05) (CHI Systems Computing in Factors rnatoso iulzto n optrGraphics Computer and Visualization on Transactions h mrcnStatistician American The Graphics Forum Graphics ls iie nOt 2 2020). 12, Oct. on visited (last EETascin nVsaiainadCmue Graphics Computer and Visualization on Transactions IEEE Txs ebun,Asrla n dto,2018. edition, 2nd Australia, Melbourne, OTexts, Athanasopoulos. George and Hyndman J. Rob 2007. graphics. data statistical in transitions Animated Robertson. George and Heer Jeffrey synergism. trace plot-density box A plots: Violin Nelson. D. Ray and Hintze L. Jerry frontiers. Visual next the for Chau. survey Horng interrogative Duen An and learning: Pienta, deep Robert in analytics Kahng, Minsuk Hohman, Fred for scatterplot series. connected time The paired Franconeri. presenting Steve and Kosara, Robert Haroz, for Steve program A II: Amelia Blackwell. Matthew data. missing and King, Gary time-series Honaker, in James values missing about do to What data. cross-section King. Gary and Honaker James models. regression data of incomplete comparison fit A to software nothing: and about methods ado data Much missing Kleinman. Ken and Horton Nicholas 2009. Technology, and In Science series. Analytics Visual time on of Symposium prediction IEEE Visual at A. presented Poster: Daniel Dayal, Castellanos. Umeshwar Malu Sharma, and K. Keim, Ratnesh Janetzko, Halldór Hao, C. Ming series. time seasonal analytics large visual A of prediction Sharma. peak-preserving K. for Ratnesh Umeshwar approach and Hill, Marwah, William Manish Keim, Mittelstädt, A. Sebastian Daniel Dayal, Janetzko, Halldór Hao, C. Ming interactive for toolkit A Prefuse: In Landay. A. visualization. James information and Card, K. Stuart Heer, Jeffrey selecting for tool maps. for online schemes An colour Colorbrewer.org: Brewer. Cynthia and Harrower Mark 1994. USA, NJ, Hamilton. D. James 29:1428,2016. 22(9):2174–2186, , ae 2–3,2009. 229–230, pages , ora fSaitclSoftware Statistical of Journal 03:9–0,2011. 30(3):691–700, , mrcnJunlo oiia Science Political of Journal American iesre analysis series Time 11:99,2007. 61(1):79–90, , 22:8–8,My1998. May 52(2):181–184, , atgahcJunl The Journal, Cartographic rceig fteSGH ofrneo Human on Conference SIGCHI the of Proceedings EETascin nVsaiainadComputer and Visualization on Transactions IEEE ae 2–3.AM 2005. ACM, 421–430. pages , rneo nvriyPes Princeton, Press, University Princeton . 57:–7 2011. 45(7):1–47, , oeatn:picpe n practice and principles Forecasting: https://otexts.com/fpp2 01:73,2003. 40(1):27–37, , 58:6429,2019. 25(8):2674–2693, , 42:6–8,2010. 54(2):561–581, , 13(6):1240–1247, , Computer Poster IEEE The 117 . Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. [IHS13] [JC14] [HS12] [IIC [KAF [HSA 118 [JSMK14] [JME10] [JLC19] [HXG02] [Jon80] [Jon85] + 13] + + 10] 08] 66:2–3,2010. 16(6):927–934, nml eeto o iulaayiso oe osmto data. consumption power Graphics of analytics visual for detection Anomaly visualization. evaluating of practice the on review systematic A for routes flight display to maps schematic of generation automatic An Vinot. Queue ls iie nOt 2 2020). 12, Oct. on visited (last Engineering nomto iulzto:HmnCnee susadPerspectives and Issues Human-Centered Visualization: Information (AVI’10) Interfaces Visual Advanced on Conference International i rffi otolr:Srcueadclrotmzto.In optimization. color and Structure controllers: traffic air eryHe n e hedra.Itrciednmc o iulanalysis. visual for dynamics Interactive Shneiderman. Ben and Heer Jeffrey nCmue cec,pgs1415 pigr 2008. Springer, 154–175. pages Science, Computer editors, in North, Chris challenges. and and Fekete, Jean-Daniel process, Stasko, Definition, T. John analytics: Kerren, Andreas Visual In Melançon. Guy Kohlham- and Jörn Görg, mer, Carsten Fekete, Jean-Daniel Andrienko, Gennady Keim, Daniel Keim. A. Daniel and Mittelstädt, Sebastian Stoffel, Florian Janetzko, Halldór In data. spaced unequally with analysis statistics series Time Jones. H. Richard with series time to models arma of observations. fitting missing likelihood Maximum Jones. H. Richard of perception Graphical Elmqvist. series. Niklas time multiple and McDonnel, Bryan Javed, Waqas interactive on advances research Recent learning. Chen. machine Changjian and Liu, Shixia Jiang, Liu 0.4-1. version package R Cloern. 2014. E. James and Jassby D. Alan Graphics Computer and Visualization on Möller. Thorsten and Sedlmair, Michael Chen, Jian Isenberg, Petra Isenberg, Tobias Inc. Global, IHS and network neural of study prediction. comparative series A time Goh. in N. modeling Thong ARIMA and Box-Jenkins Xie, Min Ho, L S. 2010. Jean-Luc and Tabart, Gilles Alonso, Roland Serrurier, Mathieu Hurter, Christophe 02:03–05,2012. 10(2):30:30–30:55, , oue5 ae 5–7.Esve,Asedm ehrad,1985. Netherlands, Amsterdam, Elsevier, 157–178. pages 5, volume , 82–7 2014. 38:27–37, , 22:7–7,2002. 42(2):371–375, , Ves eso 8 Version EViews, ora fVisualization of Journal EETascin nVsaiainadCmue Graphics Computer and Visualization on Transactions IEEE Technometrics rie A 2013. CA, Irvine, . 23:8–9,1980. 22(3):389–395, , q xlrn ae ult oioigdata monitoring quality water Exploring wq: 91)21–87 2013. 19(12):2818–2827, , 22:0–1,2019. 22(2):401–417, , http://www.eviews.com optr Industrial & Computers rceig fthe of Proceedings EETransactions IEEE ae 233–240, pages , etr Notes Lecture , optr & Computers adokof Handbook ACM , , Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. [KJL14] [KKEM10] [KPB16] [KMT09] Templ. Matthias and Schopfhauser, Daniel Meraner, Angelika Kowarik, Alexander [KMST12] [KM12] [KKA95] [KBK11] [LAB [KMS [KHP + + + 09] 11] 08] 9 (Vis95) ’95 ol o iulAnalytics Visual for Tools lmts A S,20.IEEE. 2009. USA, CA, Alamitos, 2010. 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Norway, Oslo, time of detection outlier and adjustment Interactive 2008. Heidelberg, Berlin, H. Michael Simoff, Simeon In editors, Mazeika, challenges. Arturas and and Hartmut Boehlen, Scope and Thomas, analytics: Jim Visual Schneidewind, Jörn Ziegler. Mansmann, Florian Keim, A. Daniel R-project.org/package=x12 processing batch for Meraner. structure Angelika and Kowarik Alexander Analytics Visual with Problems Solving Mansmann. - Age Florian Information and The Ellis, ing Geoffrey Kohlhammer, Jörn Keim, A. Daniel In A data. of pattern: amounts Recursive large very Ankerst. visualizing Mihael for and technique Kriegel, Hans-Peter Keim, A. Daniel long- of 2014. In visualization trend sets. Multi-scale data temperature Liu. term Jiayi and Jusufi, Ilir Kerren, Andreas data. transformations credible and and Visualizations usable wrangling: for data in directions Ham, Paolo Research and van Brodbeck, Buono. Dominique Frank Lee, Kennedy, Bongshin Weaver, Jessie Chris Riche, Plaisant, Henry Nathalie Catherine Heer, Jeffrey Kandel, Sean ae 7–8,1995. 279–286, pages , 71)23–49 2011. 17(12):2432–2439, , etr oe nCmue cec LC) Springer, (LNCS). Science Computer in Notes Lecture , nomto Visualization Information rceig fSGA 04-Vsa Computing Visual - 2014 SIGRAD of Proceedings 02 akg eso 1.0-3. version package R 2012. , e ok Y S,2016. USA, NY, York, New , ls iie nOt 2 2020). 12, Oct. on visited (last iulDt iig hoy ehiusand Techniques Theory, Mining: Data Visual CLWrso nHmnInterpretability Human on Workshop ICML IKDExplorations SIGKDD EETascin nVsaiainand Visualization on Transactions IEEE 1:X2-waprfnto and function wrapper - X12 x12: rceig fte1t In- 13th the of Proceedings 04:7–8,2011. 10(4):271–288, , rceig Visualization Proceedings 12:–,2009. 11(2):5–8, , ae 44,Los 44–49, pages , Eurographics . http://CRAN. 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Roger hzaSdq rlge eerhadpatc ndt ult aaeet In management. 2013. quality Heidelberg, data in editor, practice Sadiq, and Shazia Research Prologue: Sadiq. Shazia al. et Sprengell J. C. Applications point. breakdown and Statistics high ical with estimation Multivariate Rousseeuw. J. Peter (CMV) exploratory Visualization in views Exploratory multiple in & Coordinated art: the of State Roberts. C. Jonathan 2014. IEEE, 269–270. segmentation time-series In on influence labeling. parameter and Analyzing Miksch. Alsallakh, Bilal Silvia Bögl, and Markus Schumann, Heidrun Luboschik, Martin Röhlig, Martin by recognition activity Supporting Miksch. Silvia and Alsallakh, Schumann, Bilal Heidrun Bögl, Kirste, Markus Thomas Krüger, Frank Luboschik, Martin Röhlig, Martin of analytics visual for library software and model data. data time-oriented Silvia A and Alsallakh, TimeBench: Bilal Aigner, Miksch. Wolfgang Lammarsch, Tim Rind, Alexander Grafton. Anthony and Rosenberg Daniel In 2008. graph. horizon the of development The Reijner. Hannes 2020. Austria, Vienna, Computing, Statistical for Foundation Team. Core R rneo rhtcua rs,21.Gol-ok-D DqWqKVzipToC. Google-Books-ID: 2010. Press, Architectural Princeton . .Bnik .Femn .Gown n tes 1708. others, and Goodwin, T. Freeman, W. Bonwick, R. . :ALnug n niomn o ttsia Computing Statistical for Environment and Language A R: rceig fteCneec nCodntdadMlil Views Multiple and Coordinated on Conference the of Proceedings rceig fte21 EECneec nVsa Analytics Visual on Conference IEEE 2014 the of Proceedings rceig fte21 EECneec nVsa Analytics Visual on Conference IEEE 2015 the of Proceedings EETascin nVsaiainadCmue Graphics Computer and Visualization on Transactions IEEE poim fHportsadteSnecso ess With Celsus: of Sentences the and Hippocrates of Aphorisms adoko aaQuality Data of Handbook :8–9,1985. 8:283–297, , ae 17.IE,2007. IEEE, 61–71. pages , atgahe fTm:AHsoyo the of History A Time: of Cartographies ae –1 pigrBerlin Springer 1–11. pages , News R rceig fthe of Proceedings 4(2):10–14, , Mathemat- pages , pages , R . , , Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. [Shn96] [SDZ [SSS [SNHS13] [SMM12] [SFdOL04] [SMY [Shn83] [SBVLK09] [SAS12] [Sch99] [SS11] + + 14] + 00] 05] ihRexamples. R With iulzto InfoVis Visualization uaHsia n auiKsd.Totn suoclrn:Cmatvisualiza- Compact coloring: pseudo Two-tone Kaseda. Takumi and Hoshiya, Yuka rnatoso iulzto n optrGraphics Computer and Visualization on Transactions utpeVesi xlrtr iulzto,CMV04 Visualization, Exploratory in Views Multiple iulcutraayi ftaetr aawt neatv ooe maps. Kohonen interactive with data trajectory of analysis cluster Visual optrGraphics Computer Research iulztos In visualizations. InfoVis Computer IEEE ay C oebr2012. November NC, Cary, Inc. Institute SAS n ailA em nweg eeainmdlfrvsa analytics. visual for model generation Knowledge Ellis, Keim. Geoffrey Kwon, A. Chul Daniel Bum and Stoffel, Florian Stoffel, Andreas Sacha, Dominik Stoffer. S. David and Shumway H. Robert tasks. visualization A of Schumann. Heidrun space and Heitzler, design Magnus Nocke, Thomas Schulz, Hans-Jörg In data. one-dimensional for tion Saito, Hiroki Yamamoto, Mitsuyoshi Miyamura, Nakamura Hiroko Saito, Takafumi Graphics Computer stacks. and the and methodology: trenches study the Design from Munzner. Reflections Tamara and Meyer, Miriah Sedlmair, Michael IEEE. IEEE, 1996. information USA, for Colorado, taxonomy Boulder, type 336–343, data by task A it: have eyes The Shneiderman. Ben languages. programming beyond step A manipulation: Direct Shneiderman. Ben case A data: spatio-temporal In of Haim study. exploration and assist Oliveira, to de F. views Christina Coordinated Maria Levkowitz. Flores, F. Edilson Shimabukuro, H. Milton Cambridge, Institute, Effects Health Methods 94, 2000. I: Report Research Part study. issues. pollution Douglas methodologic air and and and Schwartz, mortality, morbidity, Joel national Zeger, The Scott Dockery. Dominici, Francesca Samet, Jonathan primer. a imputation: Multiple Schafer. Joseph oisShek ügnBrad ain o adsegr n önKohlhammer. Jörn and Landesberger, Von Tatiana Bernard, Jürgen Schreck, Tobias ae 7–8.IE,2005. IEEE, 173–180. pages , ()31,1999. 8(1):3–15, , rceig fte2dItrainlCneec nCodntdand Coordinated on Conference International 2nd the of Proceedings 68:76,1983. 16(8):57–69, , rceig 96IE ypsu nVsa Languages Visual on Symposium IEEE 1996 Proceedings pigr e ok r dto,2011. edition, 3rd York, New Springer, M 0Mdln n utvraeMethods Multivariate and Modeling 10 JMP 91)26–35 2013. 19(12):2366–2375, , ()1–9 2009. 8(1):14–29, , 81)23–40 2012. 18(12):2431–2440, , http://www.jmp.com EESmoimo nomto Visualization, Information on Symposium IEEE EETascin nVsaiainand Visualization on Transactions IEEE ieSre nlssadisApplications. its and Analysis Series Time EETascin nVisualization on Transactions IEEE ttsia ehd nMedical in Methods Statistical ls iie nOt 2 2020). 12, Oct. on visited (last ae 0–1.IE,2004. IEEE, 107–117. pages , 01)10–63 2014. 20(12):1604–1613, , A nttt Inc., Institute SAS . Information pages , IEEE 123 Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. [STA [Sta11] [Til75] 124 [vB14] [vB12] Tufte. R. Edward [Tuf06] [Tuf83] [Tsa00] [TC05] [TAKP13] [Urb20] Tk7 onW Tukey. W. John [Tuk77] [Tsa10] [The10] [TAS04] + 13] iulzto n muaino isn Values Missing of Imputation and Visualization est fteauthor the of website eeomn gnafrVsa Analytics Visual for Agenda Development X 2011. TX, Science mathworks.com StataCorp. 00;Nweiino v1]aalbe[vB18]]. available [vB12] of edition New 2020); [ Avail- from 2014] 18, Feb. at [accessed online 2014. able Online, imputation. Multiple Buuren. van Stef 2012. USA, Raton, Boca Burren. van Stef version package R 2020). 2020. interface, java to R 0.9-13. Low-level rJava: Urbanek. Simon 1983. USA, CT, Cheshire, Tufte. R. Edward 2010. edition, edition 3rd Sons, & Wiley John Statistics. Tsay. S. Ruey research. Association future Statistical and American history the Brief of forecasting: and series Time Tsay. S. Ruey graphs. experimental Early Tilling. Laura Inc. MathWorks The Cook. A. Kristin and Thomas J. James SAC’04 computing In layouts. visualiza- Axes-based radial with Schumann. tions Heidrun and Abello, James Tominski, Christian Prantner. Bernd and Kowarik, Alexander Alfons, Andreas Templ, Matthias 0.9.0. version package and Kowarik, Prantner. Alexander Bernd Alfons, Andreas Templ, Matthias Schopfhauser, Daniel vB12 ()1323 1975. 8(3):193–213, , http://CRAN.R-project.org/package=rJava http://www.stata.com tt ttsia otae ees 12 Release Software: Statistical Stata ;[paefo c.1,22:Wbientacsil nmr.New anymore. accessible not Website 2020: 13, Oct. from [Update ]; http://www.multiple-imputation.com nlsso iaca ieSeries Time Financial of Analysis xlrtr aaAnalysis Data Exploratory IGI iulzto n muaino isn Values Missing of Imputation and Visualization VIMGUI: ls iie nOt 2 2020). 12, Oct. on visited (last lxbeIptto fMsigData Missing of Imputation Flexible euiu Evidence Beautiful h iulDslyo uniaieInformation Quantitative of Display Visual The ae 2214.AM 2004. ACM, 1242–1247. pages , https://stefvanbuuren.name/ ALB(R2009a) MATLAB rceig fte20 C ypsu nApplied on symposium ACM 2004 the of Proceedings rpisPes hsie T S,2006. USA, CT, Cheshire, Press, Graphics . ls iie nOt 2 2020). 12, Oct. on visited (last 540:3–4,2000. 95(450):638–643, , luiaigtePt:TeRsac and Research The Path: the Illuminating EE o lmts A S,2005. USA, CA, Alamitos, Los IEEE, . Addison-Wesley, 1977. . h rts ora o h itr of History the for Journal British The aik A 2010. MA, Natick, . 03 akg eso 4.0.0. version package R 2013. , ie eisi rbblt and Probability in Series Wiley . ttCr P olg Station, College LP, StataCorp . hpa n Hall/CRC, and Chapman . ls iie nOt 13, Oct. on visited (last paeo pedxA Appendix of Update , ls iie nOt 12, Oct. on visited (last rpisPress, Graphics . http://www. 03 R 2013. , Journal VIM: Die approbierte gedruckte Originalversion dieser Dissertation ist an der TU Wien Bibliothek verfügbar. The approved original version of this doctoral thesis is available in print at TU Wien Bibliothek. 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Jarke nomto iulzto ecpinfrdesign for Perception - Visualization Information lxbeIptto fMsigData Missing of Imputation Flexible 2(3)2728 1927. 226(636):267–298, , 24:2–3,2006. 12(4):421–432, , 26:6015,2016. 22(6):1640–1651, , ae –,SnFacso A S,19.IEEE. 1999. USA, CA, Francisco, San 4–9, pages , ahmtc,Vrin9.0.1 Version Mathematica, rceig 99IE ypsu nInformation on Symposium IEEE 1999 Proceedings EETascin nVsaiainand Visualization on Transactions IEEE ls iie nOt 2 2020). 12, Oct. on visited (last EETascin nVisualization on Transactions IEEE hlspia rnatosof Transactions Philosophical hpa n Hall/CRC, and Chapman . hmag,I,2013. IL, Champaign, . ognKaufmann Morgan . 125