W. Aigner, S. Miksch, H. Schumann, and C. Tominski, Visualization of Time-Oriented Data, 2011.
DOI : 10.1007/978-0-85729-079-3

D. Albers, M. Correll, and M. Gleicher, Task-driven evaluation of aggregation in time series visualization, Proceedings of the 32nd annual ACM conference on Human factors in computing systems, CHI '14, pp.551-560, 2014.
DOI : 10.1145/2556288.2557200

D. Albers, C. Dewey, and M. Gleicher, Sequence Surveyor: Leveraging Overview for Scalable Genomic Alignment Visualization, IEEE Transactions on Visualization and Computer Graphics, vol.17, issue.12, pp.2392-2401, 2011.
DOI : 10.1109/TVCG.2011.232

J. Aßfalg, H. Kriegel, P. Kröger, P. Kunath, A. Pryakhin et al., Similarity search on time series based on threshold queries, Proceedings of the 10th International Conference on Advances in Database Technology, EDBT'06, pp.276-294, 1007.

T. Baguley, Serious Stats: A guide to advanced statistics for the behavioral sciences, 2012.
DOI : 10.1007/978-0-230-36355-7

G. E. Batista, E. J. Keogh, O. M. Tataw, and V. M. Souza, CID: an efficient complexity-invariant distance for time series, Data Mining and Knowledge Discovery, vol.44, issue.9, pp.634-669, 2014.
DOI : 10.1007/11612704_2

D. J. Berndt and J. Clifford, Using dynamic time warping to find patterns in time series, Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS'94, pp.359-370, 1994.

E. Bertini, P. Hertzog, and D. Lalanne, SpiralView: Towards Security Policies Assessment through Visual Correlation of Network Resources with Evolution of Alarms, 2007 IEEE Symposium on Visual Analytics Science and Technology, pp.139-146, 2007.
DOI : 10.1109/VAST.2007.4389007

R. L. Brennan and D. J. Prediger, Coefficient Kappa: Some Uses, Misuses, and Alternatives, Educational and Psychological Measurement, vol.49, issue.3, pp.687-699, 1981.
DOI : 10.1016/0001-8791(77)90052-5

P. Buono and A. L. Simeone, Interactive shape specification for pattern search in time series, Proceedings of the working conference on Advanced visual interfaces, AVI '08, pp.480-481, 2008.
DOI : 10.1145/1385569.1385666

L. Byron and M. Wattenberg, Stacked Graphs ??? Geometry & Aesthetics, IEEE Transactions on Visualization and Computer Graphics, vol.14, issue.6, pp.1245-1252, 2008.
DOI : 10.1109/TVCG.2008.166

A. Canty and B. D. Ripley, boot: Bootstrap R (S-Plus) Functions, 2017. R package version 1, pp.3-20

Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall et al., The ucr time series classification archive, 2015.

Y. Chen, M. Nascimento, B. C. Ooi, and A. K. Tung, SpADe: On Shape-based Pattern Detection in Streaming Time Series, 2007 IEEE 23rd International Conference on Data Engineering, pp.786-795367924, 2007.
DOI : 10.1109/ICDE.2007.367924

M. Correll, D. Albers, S. Franconeri, and M. Gleicher, Comparing averages in time series data, Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, CHI '12, pp.1095-1104, 2012.
DOI : 10.1145/2207676.2208556

M. Correll and M. Gleicher, The semantics of sketch: Flexibility in visual query systems for time series data, 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), pp.131-140, 2016.
DOI : 10.1109/VAST.2016.7883519

M. De-curtis, J. G. Jefferys, and M. Avoli, Interictal Epileptiform Discharges in Partial Epilepsy, pp.303-325, 2012.
DOI : 10.1093/med/9780199746545.003.0017

H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, Querying and mining of time series data, Proceedings of the VLDB Endowment, vol.1, issue.2, pp.1542-1552, 2008.
DOI : 10.14778/1454159.1454226

P. Dragicevic, Fair Statistical Communication in HCI, Modern Statistical Methods for HCI, pp.291-330, 2016.
DOI : 10.1007/978-3-319-26633-6_13

URL : https://hal.archives-ouvertes.fr/hal-01377894

B. Efron, Better Bootstrap Confidence Intervals, Journal of the American Statistical Association, vol.11, issue.397, pp.171-185, 1987.
DOI : 10.1214/aos/1176346163

P. Eichmann and E. Zgraggen, Evaluating Subjective Accuracy in Time Series Pattern-Matching Using Human-Annotated Rankings, Proceedings of the 20th International Conference on Intelligent User Interfaces, IUI '15, pp.28-37, 2015.
DOI : 10.1145/634067.634292

C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, Fast subsequence matching in time-series databases, Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, SIGMOD '94, pp.419-429, 1994.

T. Fu, A review on time series data mining, Engineering Applications of Artificial Intelligence, vol.24, issue.1, pp.164-181
DOI : 10.1016/j.engappai.2010.09.007

J. Fuchs, F. Fischer, F. Mansmann, E. Bertini, and P. Isenberg, Evaluation of alternative glyph designs for time series data in a small multiple setting, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '13, pp.3237-3246, 2013.
DOI : 10.1145/2470654.2466443

URL : https://hal.archives-ouvertes.fr/hal-00781504

A. Gogolou, T. Tsandilas, T. Palpanas, and A. Bezerianos, Comparing time series similarity perception under different color interpolations, p.6, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01844994

D. Q. Goldin and P. C. Kanellakis, On similarity queries for time-series data: Constraint specification and implementation, Proceedings of the First International Conference on Principles and Practice of Constraint Programming, CP '95, pp.137-153, 1995.
DOI : 10.1007/3-540-60299-2_9

M. Gregory and B. Shneiderman, Shape Identification in Temporal Data Sets, 2009.
DOI : 10.1007/978-1-4471-2804-5_17

K. L. Gwet, Handbook of Inter-Rater Reliability, 4th Edition: The Definitive Guide to Measuring The Extent of Agreement Among Raters

J. Heer, N. Kong, and M. Agrawala, Sizing the horizon, Proceedings of the 27th international conference on Human factors in computing systems, CHI 09, pp.1303-1312, 2009.
DOI : 10.1145/1518701.1518897

H. Hochheiser and B. Shneiderman, Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration, Information Visualization, vol.5, issue.1, pp.1-18, 2004.
DOI : 10.1145/312624.312676

C. Holz and S. Feiner, Relaxed selection techniques for querying timeseries graphs, Proceedings of the 22Nd Annual ACM Symposium on User Interface Software and Technology, UIST '09, pp.213-222, 2009.

K. Indiradevi, E. Elias, P. Sathidevi, S. D. Nayak, and K. Radhakrishnan, A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram, Computers in Biology and Medicine, vol.38, issue.7, pp.805-816, 2008.
DOI : 10.1016/j.compbiomed.2008.04.010

A. Jabbari, R. Blanch, and S. Dupuy-chessa, Composite Visual Mapping for Time Series Visualization, 2018 IEEE Pacific Visualization Symposium (PacificVis), pp.116-124, 2018.
DOI : 10.1109/PacificVis.2018.00023

URL : https://hal.archives-ouvertes.fr/tel-01913579

W. Javed, B. Mcdonnel, and N. Elmqvist, Graphical Perception of Multiple Time Series, IEEE Transactions on Visualization and Computer Graphics, vol.16, issue.6, pp.927-934, 2010.
DOI : 10.1109/TVCG.2010.162

J. Jing, J. Dauwels, T. Rakthanmanon, E. Keogh, S. Cash et al., Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping, Journal of Neuroscience Methods, vol.274, pp.179-190, 2016.
DOI : 10.1016/j.jneumeth.2016.02.025

S. Juan-orta, D. , C. Kh, Q. Az, C. Dj et al., Prognostic Implications of Periodic Epileptiform Discharges, Archives of Neurology, vol.66, issue.8, pp.985-991, 2009.
DOI : 10.1001/archneurol.2009.137

R. Kincaid and H. Lam, Line graph explorer, Proceedings of the working conference on Advanced visual interfaces , AVI '06, pp.404-411, 2006.
DOI : 10.1145/1133265.1133348

H. Levkowitz and G. Herman, Color Scales For Image Data: Design And Evaluation, Computer Graphics and Applications), vol.12, issue.1, pp.82-89, 1992.
DOI : 10.1007/978-0-585-28428-6_7

E. Limpert, W. A. Stahel, and M. Abbt, Log-normal Distributions across the Sciences: Keys and Clues, BioScience, vol.31, issue.16, pp.341-346, 2001.
DOI : 10.2105/AJPH.42.11.1403

E. K. Louis and L. C. Frey, Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants, 2016.

M. Mannino and A. Abouzied, Expressive time series querying with handdrawn scale-free sketches, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI '18, pp.1-388, 2018.

P. Mclachlan, T. Munzner, E. Koutsofios, and S. North, LiveRAC, Proceeding of the twenty-sixth annual CHI conference on Human factors in computing systems , CHI '08, pp.1483-1492, 2008.
DOI : 10.1145/1357054.1357286

W. Müller and H. Schumann, Visualization for modeling and simulation: Visualization methods for time-dependent data -an overview, Proceedings of the 35th Conference on Winter Simulation: Driving Innovation, WSC '03, pp.737-745, 2003.

P. K. Muthumanickam, K. Vrotsou, M. Cooper, and J. Johansson, Shape grammar extraction for efficient query-by-sketch pattern matching in long time series, 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), pp.121-130, 2016.
DOI : 10.1109/VAST.2016.7883518

D. Nadalutti and L. Chittaro, Visual analysis of users??? performance data in fitness activities, Computers & Graphics, vol.31, issue.3, pp.429-439, 2007.
DOI : 10.1016/j.cag.2007.01.032

T. Palpanas, Data Series Management, ACM SIGMOD Record, vol.44, issue.2, pp.47-52, 2015.
DOI : 10.1145/2783258.2783382

C. Perin, F. Vernier, and J. Fekete, Interactive horizon graphs, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '13, pp.3217-3226, 2013.
DOI : 10.1145/2470654.2466441

URL : https://hal.archives-ouvertes.fr/hal-00781390

T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover et al., Searching and mining trillions of time series subsequences under dynamic time warping, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.262-270, 1145.
DOI : 10.1145/2339530.2339576

C. A. Ratanamahatana and E. Keogh, Everything you know about dynamic time warping is wrong, Third Workshop on Mining Temporal and Sequential Data. Citeseer, 2004.

H. Reijner, The development of the horizon graph, 2008.

H. G. Rey, C. Pedreira, and R. Q. Quiroga, Past, present and future of spike sorting techniques, Brain Research Bulletin, vol.119, pp.106-117, 2015.
DOI : 10.1016/j.brainresbull.2015.04.007

K. Ryall, N. Lesh, T. Lanning, D. Leigh, H. Miyashita et al., QueryLines, CHI '05 extended abstracts on Human factors in computing systems , CHI '05, pp.1765-1768, 2005.
DOI : 10.1145/1056808.1057017

T. Saito, H. N. Miyamura, M. Yamamoto, H. Saito, Y. Hoshiya et al., Two-tone pseudo coloring: Compact visualization for onedimensional data, Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization, INFOVIS '05, pp.23-33, 2005.

T. Siddiqui, A. Kim, J. Lee, K. Karahalios, and A. Parameswaran, Effortless data exploration with zenvisage, Proc. VLDB Endow, pp.457-468, 2016.
DOI : 10.14778/3025111.3025126

R. L. Spitzer and J. L. Fleiss, A Re-analysis of the Reliability of Psychiatric Diagnosis, British Journal of Psychiatry, vol.125, issue.587, pp.341-347, 1974.
DOI : 10.1192/bjp.125.4.341

K. J. Staley and F. E. Dudek, Interictal Spikes and Epileptogenesis, Epilepsy Currents, vol.6, issue.6, pp.199-202, 2006.
DOI : 10.1111/j.1535-7511.2006.00145.x

K. J. Staley, A. White, and F. E. Dudek, Interictal spikes: Harbingers or causes of epilepsy? Neuroscience letters 497, pp.247-250, 2011.

M. Stone, In Color Perception, Size Matters, IEEE Computer Graphics and Applications, vol.32, issue.2, pp.8-13, 2012.
DOI : 10.1109/MCG.2012.37

B. Swihart, B. Caffo, B. James, M. Strand, B. Schwartz et al., Lasagna Plots, Epidemiology, vol.21, issue.5, pp.621-625, 2010.
DOI : 10.1097/EDE.0b013e3181e5b06a

J. Talbot, J. Gerth, and P. Hanrahan, An Empirical Model of Slope Ratio Comparisons, IEEE Transactions on Visualization and Computer Graphics, vol.18, issue.12, pp.2613-2620, 2012.
DOI : 10.1109/TVCG.2012.196

T. Tsandilas, Fallacies of Agreement, ACM Transactions on Computer-Human Interaction, vol.25, issue.3, 2018.
DOI : 10.1177/1094428105280059

URL : https://hal.archives-ouvertes.fr/hal-01788775

E. R. Tufte, The Visual Display of Quantitative Information, 1986.

J. J. Van-wijk and E. R. Van-selow, Cluster and calendar based visualization of time series data, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99), p.801851, 1999.
DOI : 10.1109/INFVIS.1999.801851

M. Wattenberg, Sketching a graph to query a time-series database, CHI '01 extended abstracts on Human factors in computing systems , CHI '01, pp.381-382, 2001.
DOI : 10.1145/634067.634292

M. Weber, M. Alexa, and W. Müller, Visualizing time-series on spirals, IEEE Symposium on Information Visualization, 2001. INFOVIS 2001., p.963273, 2001.
DOI : 10.1109/INFVIS.2001.963273

URL : http://www.igd.fhg.de/~alexa/paper/spiral.pdf

J. Zhao, F. Chevalier, and R. Balakrishnan, KronoMiner, Proceedings of the 2011 annual conference on Human factors in computing systems, CHI '11, pp.1737-1746, 1145.
DOI : 10.1145/1978942.1979195

J. Zhao, F. Chevalier, E. Pietriga, and R. Balakrishnan, Exploratory Analysis of Time-Series with ChronoLenses, IEEE Transactions on Visualization and Computer Graphics, vol.17, issue.12, pp.2422-2431, 0195.
DOI : 10.1109/TVCG.2011.195

URL : https://hal.archives-ouvertes.fr/inria-00637082

K. Zoumpatianos, S. Idreos, and T. Palpanas, RINSE, Proceedings of the VLDB Endowment, vol.8, issue.12, pp.1912-1915, 2015.
DOI : 10.14778/2824032.2824099

K. Zoumpatianos, S. Idreos, and T. Palpanas, ADS: the adaptive data series index, The VLDB Journal, vol.8, issue.12, pp.843-866, 2016.
DOI : 10.1109/ICDE.2014.6816724