Bao Dien Quoc Nguyen
ABSTRACT
Visual analytics combines the capabilities of computers and humans to explore the insight of data. It provides coupling interactive visual representations with underlying analytical processes (e.g., visual feature extraction) so that users can utilize their cognitive and reasoning capabilities to perform complex tasks effectively or to make decisions. This paper applies successfulness of visual analytics to multivariate temporal data by proposing an interactive web prototype and an approach that enables users to explore data and detect visual features of interest. A list of nonparametric quantities is proposed to extract visual patterns of time series as well as to compute the similarity between them. The prototype integrates visualization and dimensional reduction techniques to support the exploration processes. Many different temporal datasets are used to justify the effectiveness of this approach, and some remarkable results are presented to show its value.
- Mohammed Ali, Mark W Jones, Xianghua Xie, and Mark Williams. 2019. TimeCluster: dimension reduction applied to temporal data for visual analytics. The Visual Computer 35, 6--8 (2019), 1013--1026.Google Scholar
Digital Library
- Kimberly Amadeo. 2019. History of Recessions in the United States. website. Retrieved January 30, 2020 from https://www.thebalance.com/the-history-of-recessions-in-the-united-states-3306011.Google Scholar
- G.H. Ball and D.J. Hall. 1965. Isodata, a Novel Method of Data Analysis and Pattern Classification. Stanford Research Institute. https://books.google.com/books?id=Ti3BGwAACAAJGoogle Scholar
- Etienne Becht, Leland McInnes, John Healy, Charles-Antoine Dutertre, Immanuel WH Kwok, Lai Guan Ng, Florent Ginhoux, and Evan W Newell. 2019. Dimensionality reduction for visualizing single-cell data using UMAP. Nature biotechnology 37, 1 (2019), 38.Google Scholar
- Joachim A Behar, Aviv A Rosenberg, Ido Weiser-Bitoun, Ori Shemla, Alexan- dra Alexandrovich, Evgene Konyukhov, and Yael Yaniv. 2018. PhysioZoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data. Frontiers in physiology 9 (2018), 1390.Google Scholar
- J. Bernard, M. Hutter, M. Zeppelzauer, D. Fellner, and M. Sedlmair. 2018. Com- paring Visual-Interactive Labeling with Active Learning: An Experimental Study. IEEE Transactions on Visualization and Computer Graphics 24, 1 (Jan 2018), 298--308. https://doi.org/10.1109/TVCG.2017.2744818Google ScholarCross Ref
- Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D3 data-driven documents. IEEE transactions on visualization and computer graphics 17, 12 (2011), 2301--2309.Google ScholarDigital Library
- Peter J Brockwell and Richard A Davis. 2016. Introduction to time series and forecasting. springer.Google Scholar
- Robert B Cleveland, William S Cleveland, Jean E McRae, and Irma Terpenning. 1990. STL: A seasonal-trend decomposition. Journal of official statistics 6, 1 (1990), 3--73.Google Scholar
- Tuan Nhon Dang, Anushka Anand, and Leland Wilkinson. 2013. TimeSeer: Scagnostics for high-dimensional time series. IEEE Transactions on Visualization and Computer Graphics 19, 3 (2013), 470--483. https://doi.org/10.1109/TVCG.2012.128Google ScholarDigital Library
- Tuan Nhon Dang and Leland Wilkinson. 2013. TimeExplorer: Similarity search time series by their signatures. In International Symposium on Visual Computing. Springer, 280--289.Google ScholarDigital Library
- Tuan Nhon Dang and Leland Wilkinson. 2014. ScagExplorer: Exploring scatter- plots by their scagnostics. IEEE Pacific Visualization Symposium (2014), 73--80. https://doi.org/10.1109/PacificVis.2014.42Google ScholarDigital Library
- Jon Danielsson. 2011. Financial risk forecasting: the theory and practice of forecasting market risk with implementation in R and Matlab. Vol. 588. John Wiley & Sons.Google Scholar
- Samarjit Das. 1994. Time series analysis.Google Scholar
- Ben D Fulcher and Nick S Jones. 2014. Highly comparative feature-based time- series classification. IEEE Transactions on Knowledge and Data Engineering 26, 12 (2014), 3026--3037.Google ScholarCross Ref
- Ben D Fulcher and Nick S Jones. 2017. hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell systems 5, 5 (2017), 527--531.Google Scholar
- Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, 23 (2000), e215-e220.Google ScholarCross Ref
- John A. Hartigan. 1975. Clustering Algorithms (99th ed.). John Wiley & Sons, Inc., New York, NY, USA.Google ScholarDigital Library
- DM Hawkins. 1980. Monographs on Applied Probability and Statistics.Google Scholar
- Boris Iglewicz and David Hoaglin. 1993. Volume 16: how to detect and handle outliers. The ASQC basic references in quality control: statistical techniques 16 (1993).Google Scholar
- Boris Iglewicz and David Caster Hoaglin. 1993. How to detect and handle outliers. Vol. 16. Asq Press.Google Scholar
- A. K. Jain, M. N. Murty, and P. J. Flynn. 1999. Data Clustering: A Review. ACM Comput. Surv. 31, 3 (Sept. 1999), 264--323. https://doi.org/10.1145/331499.331504Google ScholarDigital Library
- Yanfei Kang, Rob J Hyndman, and Kate Smith-Miles. 2017. Visualising forecasting algorithm performance using time series instance spaces. International Journal of Forecasting 33, 2 (2017), 345--358.Google ScholarCross Ref
- Eamonn Keogh, Stefano Lonardi, and Bill'Yuan-chi' Chiu. 2002. Finding surprising patterns in a time series database in linear time and space. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 550--556.Google ScholarDigital Library
- Ragnar H Lesch, Yannick Caillé, and David Lowe. 1999. Component analysis in financial time series. In Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering (CIFEr)(IEEE Cat. No. 99TH8408). IEEE, 183--190.Google ScholarCross Ref
- Yuan Li, Jessica Lin, and Tim Oates. 2012. Visualizing variable-length time series motifs. In Proceedings of the 2012 SIAM international conference on data mining. SIAM, 895--906.Google ScholarCross Ref
- Jessica Lin, Eamonn Keogh, and Stefano Lonardi. 2005. Visualizing and discover- ing non-trivial patterns in large time series databases. Information visualization 4, 2 (2005), 61--82.Google Scholar
- Carl H Lubba, Sarab S Sethi, Philip Knaute, Simon R Schultz, Ben D Fulcher, and Nick S Jones. 2019. catch22: CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery 33, 6 (2019), 1821-1852.Google ScholarDigital Library
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579--2605.Google Scholar
- Spyros Makridakis and Michele Hibon. 2000. The M3-Competition: results, conclusions and implications. International journal of forecasting 16, 4 (2000), 451--476.Google ScholarCross Ref
- Henry B Mann. 1945. Nonparametric tests against trend. Econometrica: Journal of the Econometric Society (1945), 245--259.Google Scholar
- Robert McGill, John W Tukey, and Wayne A Larsen. 1978. Variations of box plots. The American Statistician 32, 1 (1978), 12--16.Google ScholarCross Ref
- Leland McInnes, John Healy, and James Melville. 2018. UMAP: Uni- form Manifold Approximation and Projection for Dimension Reduction. arXiv:stat.ML/1802.03426Google Scholar
- Alex Nanopoulos, Rob Alcock, and Yannis Manolopoulos. 2001. Feature-based classification of time-series data. International Journal of Computer Research 10, 3 (2001), 49--61.Google ScholarDigital Library
- NIST/SEMATECH. 2013. e-Handbook of Statistical Methods. e-handbook. Re- trieved January 3, 2020 from https://www.itl.nist.gov/div898/handbook/index. htm.Google Scholar
- U.S. Bureau of Labor Statistics. 2019. 2020 Census and CES Employment by State. website. Retrieved January 30, 2020 from https://www.bls.gov/sae/additional-resources/ces-state-and-area-census-2020-workers.htm.Google Scholar
- Bernard Rosner. 1983. Percentage points for a generalized ESD many-outlier procedure. Technometrics 25, 2 (1983), 165--172.Google ScholarCross Ref
- Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P Boedihardjo, Crystal Chen, Susan Frankenstein, and Manfred Lerner. 2014. Grammarviz 2.0: a tool for grammar-based pattern discovery in time series. In Joint European conference on machine learning and knowledge discovery in databases. Springer, 468--472.Google ScholarDigital Library
- National Park Service. 2017. Hurricanes Irma and Maria. website. Retrieved January 30, 2020 from https://www.nps.gov/viis/learn/nature/2017-hurricanes. htm.Google Scholar
- Ori Shemla and Joachim Behar. 2019. PhysioZoo - mammalian NSR databases. https://doi.org/10.13026/P63Q-HQ95Google ScholarCross Ref
- Ruey S Tsay. 1988. Outliers, level shifts, and variance changes in time series. Journal of forecasting 7, 1 (1988), 1--20.Google ScholarCross Ref
- Laurens Van Der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. The Journal of Machine Learning Research 15, 1 (2014), 3221--3245.Google ScholarDigital Library
- Laurens Van Der Maaten, Eric Postma, and Jaap Van den Herik. 2009. Dimensionality reduction: a comparative. J Mach Learn Res 10, 66--71 (2009), 13.Google Scholar
- Michail Vlachos, Philip Yu, and Vittorio Castelli. 2005. On periodicity detection and structural periodic similarity. In Proceedings of the 2005 SIAM international conference on data mining. SIAM, 449--460.Google ScholarCross Ref
- Tien Hoang-Thuy Vo, Tran Luu-Nha Dang, Ngan Vuong-Thuy Nguyen, and Tuan Van Huynh. 2019. Classification Electroencephalography Using Machine Learn- ing. In 2019 19th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 14--19.Google Scholar
- Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemometrics and intelligent laboratory systems 2, 1--3 (1987), 37--52.Google Scholar
- Kiyoung Yang and Cyrus Shahabi. 2004. A PCA-based similarity measure for multivariate time series. In Proceedings of the 2nd ACM international workshop on Multimedia databases. 65--74.Google ScholarDigital Library
- Matthew Yglesias. 2013. The U.S. Virgin Islands Are in a Catastrophic Recession. website. Retrieved January 30, 2020 from https://slate.com/business/2013/08/virgin-islands-recession.html.Google Scholar
Index Terms
- Visual Features for Multivariate Time Series
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