1. A. Shifaz, C. Pelletier, F. Petitjean and G. Webb, “Elastic Similarity Measures for Multivariate Time Series Classification.”
2. Y. Jin, W. Buntine, F. Petitjean and G. Webb, “Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning.”
3. L-K. Lee, N. Piatkowski, F. Petitjean and G. Webb, “Computing Divergences between Discrete Decomposable Models.”

Google Scholar has this on me.

2021

67. ISPRS Journal of Photogrammetry and Remote Sensing: L. Zhu, G. Webb, M. Yebra, G. Scortechini, L. Miller and F. Petitjean, “Live fuel moisture content estimation from MODIS: A deep learning approach.”
68. Machine Learning: B. Lucas, C. Pelletier, D. Schmidt, G. Webb and F. Petitjean, “A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping.”
69. Pattern Recognition: G. Webb and F. Petitjean, “Tighter lower bounds for Dynamic Time Warping.”
70. Data Mining and Knowledge Discovery: C. Tan, C. Bergmeir, F. Petitjean, and G. Webb, “Time series extrinsic regression.”
71. Information Research: M. Weber, R. Giblin, Y. Ding and F. Petitjean, “Exploring the circulation of digital audiobooks: Australian library lending 2006-2017.”

2020

62. Data Mining and Knowledge Discovery: A. Dempster, F. Petitjean and G. Webb, “ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels.”
63. Data Mining and Knowledge Discovery: H. Ismail Fawaz, B. Lucas, G. Forestier, C. Pelletier, D. Schmidt, J. Weber, G. Webb, L. Idoumghar, P-A. Muller and F. Petitjean, “InceptionTime: Finding AlexNet for Time Series Classification.”.
64. Data Mining and Knowledge Discovery: A. Shifaz, C. Pelletier, F. Petitjean and G. Webb, “TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification.” [BibTex]

65. Data Mining and Knowledge Discovery: C. Tan, F. Petitjean and G. Webb, “FastEE: Fast Ensembles of Elastic Distances for time series classification.” [BibTex]

66. Knowledge and Information Systems: H. Zhang, F. Petitjean and W. Buntine, “Bayesian Network Classifiers using Ensembles and Smoothing, ” to appear. [BibTex]

67. Water Resources Research: J. Pudashine, A. Guyot, F. Petitjean, V. Pauwels, R. Uijlenhoet, A. Seed, M. Prakash and J. Walker, “Deep Learning for an improved prediction of rainfall retrievals from commercial microwave links.”
68. DSAA: R. Fischer, N. Piatkowski, C. Pelletier, G. Webb, F. Petitjean and K Morik, “No cloud on the horizon: Probabilistic gap filling in satellite image series.”.
69. PAKDD: H. Zhang, F. Petitjean and W. Buntine, “Hierarchical Gradient Smoothing for Probability Estimation Trees, ” to appear. [BibTex]

2019

54. Remote Sensing: C. Pelletier, G. Webb and F. Petitjean, “Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series.” [BibTex]

55. Data Mining and Knowledge Discovery: B. Lucas, A. Shifaz, C. Pelletier, L. O'Neill, N. Zaidi, B. Goethals, F. Petitjean, G. Webb, “Proximity Forest: An effective and scalable distance-based classifier for time series.” [BibTex]

56. University of New South Wales Law Journal: J. Flynn, R. Giblin and F. Petitjean, “What Happens When Books Enter the Public Domain? Testing Copyright's Underuse Hypothesis Across Australia, New Zealand, the United States and Canada,” in press. [BibTex]

57. Information Research: R. Giblin, J. Kennedy, K. Weatherall, D. Gilbert, J. Thomas and F. Petitjean, “Available - But not Accessible? Investigating Publisher e-lending Licensing Practices,” in press. [BibTex]

58. Information Research: R. Giblin, J. Kennedy, C. Pelletier, J. Thomas, K. Weatherall and F. Petitjean, “What Can 100,000 Books Tell Us about the International Public Library e-lending Landscape?,” in press. [BibTex]

59. SIAM SDM: C. Tan, F. Petitjean and G. Webb, “Elastic bands across the path: A new framework and method to lower bound DTW,” to appear. [BibTex]

60. IEEE IGARSS: C. Pelletier, G. Webb and F. Petitjean, “Deep Learning for the Classification of Sentinel-2 Image Time Series.” [BibTex]

61. AIME: H. Ismail Fawaz, G. Forestier, J. Weber, F. Petitjean, L. Idoumghar and P.-A. Muller , “Automatic alignment of surgical videos using kinematic data. [BibTex]

2018

46. Machine Learning: F. Petitjean, W. Buntine, G. Webb, N. Zaidi, “Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes.” [BibTex]

47. Data Mining and Knowledge Discovery: G. Webb, L. Lee, B. Goethals and F. Petitjean, “Analyzing concept drift and shift from sample data,” in press. [BibTex]

48. Data Mining and Knowledge Discovery: H.A. Dau, D.F. Silva, F. Petitjean, G. Forestier, A. Bagnall and E. Keogh, “Optimizing Dynamic Time Warping's Window Width for Time Series Data Mining Applications,” in press. [BibTex]

49. Artificial Intelligence in Medicine: G. Forestier, F. Petitjean, P. Senin, F. Despinoy, A. Huaulmé, H. Ismail Fawaz, J. Weber, L. Idoumghar, P-A Muller and P. Jannin, “Surgical motion analysis using discriminative interpretable patterns,” in press. [BibTex]

50. SIAM SDM: C. Tan, M. Herrmann, G. Forestier, G. Webb and F. Petitjean, “Efficient search of the best warping window for Dynamic Time Warping,” Best Paper Award. [BibTex]

51. SIAM SDM: N. Zaidi, F. Petitjean and G. Webb, “Efficient and Effective Accelerated Hierarchical Higher-OrderLogistic Regression for Large Data Quantities.” [BibTex]

52. ECML/PKDD: J. Capdevila, J. Cerquides, J. Torres, F. Petitjean and W. Buntine, “A Left-to-right Algorithm for Likelihood Estimation in Gamma-Poisson Factor Analysis,” in press.
53. Behaviormetrika: Joan Capdevila, He Zhao, F. Petitjean and W. Buntine, “Experiments with learning graphical models on text, ” in press.

2017

38. IEEE ICDM: G. Forestier, F. Petitjean, H.A. Dau, G. Webb, and E. Keogh, “Generating synthetic time series to augment sparse datasets.” [BibTex]

39. SIAM SDM: C. Tan, G. Webb and F. Petitjean, “Indexing and classifying gigabytes of time series under time warping.” [BibTex]

40. Machine Learning: N. Zaidi, G. Webb, M. Carman and F. Petitjean, “Efficient Parameter Learning of Bayesian Network Classifiers.” [BibTex]

41. IEEE Big Data: H.A. Dau, D.F. Silva, F. Petitjean, G. Forestier, A. Bagnall and E. Keogh, “Judicious Setting of Dynamic Time Warping's Warping Window Width allows more Accurate Classification of Time Series.”
42. Artificial Intelligence in Medicine: G. Forestier, F. Petitjean, P. Senin, L. Rifaud, P.-L. Henaux and P. Jannin, “Finding discriminative and interpretable patterns in sequences of surgical activities.”
43. AIME: G. Forestier, F. Petitjean, L. Riffaud & P. Jannin, “Discovering Discriminative and Interpretable Patterns for Surgical Motion Analysis.”
44. Artificial Intelligence in Medicine: G. Forestier, F. Petitjean, L. Riffaud & P. Jannin, “Automatic matching of surgeries to predict surgeons' next actions.”
45. JCO Clinical Cancer Informatics : M. Ananda-Rajah, C. Bergmeir, F. Petitjean, M. Slavin, K. Thursky and G. Webb, “Towards electronic surveillance of invasive mold diseases in haematology-oncology patients: an expert system combining natural language processing of chest computed tomography reports, microbiology and antifungal drug data,” in press

2016

30. Machine Learning: N. Zaidi, G. Webb, M. Carman and F. Petitjean, “ALRⁿ: Accelerated Higher-Order Logistic Regression.”
31. KDD: G. Webb and F. Petitjean, “A multiple test correction for streams and cascades of statistical hypothesis test.”
32. Data Mining and Knowledge Discovery: F. Petitjean, T. Li, N. Tatti and G. Webb, “Skopus: Mining top-k sequential patterns under Leverage.”
33. Data Mining and Knowledge Discovery: G. Webb, R. Hyde, H. Cao, H. Nguyen and F. Petitjean, “Characterizing Concept Drift.”
34. PAKDD: N. Zaidi, F. Petitjean and G. Webb, “Pre-conditioning an Artificial Neural Network using Naive Bayes,” 2016.
35. Knowledge and Information Systems: F. Petitjean, G. Forestier, G. Webb, A. Nicholson, Y. Chen and E. Keogh, “Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm,” in press.

2015

24. SIAM SDM: F. Petitjean and G. Webb, “Scaling log-linear analysis to datasets with thousands of variables. ” Best Paper Honorable Mention.
25. AIME: G. Forestier, F. Petitjean, L. Riffaud & P. Jannin, “Optimal sub-sequence matching for the automatic prediction of surgical tasks.” Best Paper Award.

2014

22. IEEE ICDM: F. Petitjean, G. Forestier, G. Webb, A. Nicholson, Y. Chen and E. Keogh, “Dynamic Time Warping Averaging of Time Series allows Faster and more Accurate Classification.” Nominated for Best paper award (top 1%)
23. IEEE ICDM: F. Petitjean, L. Allison, G. Webb, “A statistically efficient and scalable method for log-linear analysis of high-dimensional data.” Nominated for Best paper award (top 1%)
24. Artificial Intelligence in Medicine: G. Forestier, F. Petitjean, L. Riffaud & P. Jannin, “Non-linear temporal scaling of surgical processes,” 2014.
25. IEEE Geoscience and Remote Sensing Letters: F. Petitjean & J. Weber, “Efficient satellite image time series analysis under time warping,” 2014. [BibTex]

26. International Journal of Remote Sensing 2014: F. Petitjean, J. Inglada & P. Gançarski, “Assessing the quality of temporal high-resolution classifications with low-resolution satellite images time series,” 2014. [BibTex]

2013

17. IEEE ICDM: F. Petitjean, G. Webb and A. Nicholson, “Scaling log-linear analysis to high-dimensional data.”
18. IEEE IGARSS: F. Petitjean, J. Inglada & P. Gançarski, “Detecting land-cover modifications from multi-resolution satellite image time series.”

2012

15. Pattern Recognition Letters: F. Petitjean, C. Kurtz, N. Passat & P. Gançarski, “Spatio-Temporal Reasoning for the Classification of Satellite Image Time Series,” 2012. [BibTex]

16. IEEE Transactions on Geoscience and Remote Sensing: F. Petitjean, J. Inglada & P. Gançarski, “Satellite Image Time Series Analysis under Time Warping,” 2012. [BibTex]

17. IEEE IGARSS: F. Petitjean, J. Inglada & P. Gançarski, “Introducing prior knowledge in temporal distances for Satellite Image Time Series analysis.”
18. IEEE IGARSS: F. Petitjean, A. Puissant & P. Gançarski, “Monitoring urban sprawl from Satellite Image Time Series.”
19. IEEE IGARSS: J. Weber, F. Petitjean & P. Gançarski, “Towards efficient satellite image time series analysis: combination of Dynamic Time Warping and Quasi-Flat Zones.”
20. Theoretical Computer Science: F. Petitjean & P. Gançarski, “Summarizing a Set of Time Series by Averaging:
    from Steiner Sequence to Compact Multiple Alignment,” 2012.

2011

9. IEEE Multi-Temp: F. Petitjean, J. Inglada & P. Gançarski, “Clustering of satellite image time series under time warping.”
10. IEEE IGARSS: F. Petitjean, J. Inglada & P. Gançarski, “Temporal Domain Adaptation under Time Warping.”
11. IEEE IGARSS: C. Kurtz, F. Petitjean & P. Gançarski, “A context-based approach for the classification of Satellite Image Time Series.”
12. International Journal of Neural Systems: F. Petitjean F. Masseglia, P. Gançarski & G. Forestier, “Discovering Significant Evolution Patterns from Satellite Image Time Series.”
13. Pattern Recognition: F. Petitjean, A. Ketterlin & P. Gançarski, “A global averaging method for dynamic time warping, with applications to clustering.” Ranked 8^th hottest article in Elsevier's Computer Science journals.
14. EGC: F. Petitjean, F. Masseglia & P. Gançarski, “Découverte de motifs d'évolution significatifs dans les séries temporelles d'images satellites.”

2010

1. IDEAL: F. Petitjean, P. Gançarski, F. Masseglia & G. Forestier, “Analysing Satellite Image Time Series by means of Pattern Mining.”
2. SFC: F. Petitjean, P. Gançarski & A. Ketterlin, “Une solution pour l'application de Dynamic Time Warping au clustering.”
3. SAGEO: F. Petitjean, P. Gançarski & F. Masseglia, “Extraction de motifs d'évolution dans les Séries Temporelles d'Images Satellites.”