Grants

Awards

I have been fortunate to receive the following awards:

Working papers

Comments most welcome!

  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.”

Published Papers

Google Scholar has this on me.

2021

  1. Data Mining and Knowledge Discovery: 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.”
  2. Pattern Recognition: G. Webb and F. Petitjean, “Tighter lower bounds for Dynamic Time Warping.”
  3. Data Mining and Knowledge Discovery: C. Tan, C. Bergmeir, F. Petitjean, and G. Webb, “Time series extrinsic regression.”
  4. Information Research: M. Weber, R. Giblin, Y. Ding and F. Petitjean, “Exploring the circulation of digital audiobooks: Australian library lending 2006–2017.”

2020

  1. Data Mining and Knowledge Discovery: A. Dempster, F. Petitjean and G. Webb, “ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels.”
  2. 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.”.
  3. 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.” []
  4. Data Mining and Knowledge Discovery: C. Tan, F. Petitjean and G. Webb, “FastEE: Fast Ensembles of Elastic Distances for time series classification.” []
  5. Knowledge and Information Systems: H. Zhang, F. Petitjean and W. Buntine, “Bayesian Network Classifiers using Ensembles and Smoothing, ” to appear. []
  6. 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.”
  7. 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.”.
  8. PAKDD: H. Zhang, F. Petitjean and W. Buntine, “Hierarchical Gradient Smoothing for Probability Estimation Trees, ” to appear. []

2019

  1. Remote Sensing: C. Pelletier, G. Webb and F. Petitjean, “Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series.” []
  2. 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.” []
  3. 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. []
  4. 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. []
  5. 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. []
  6. 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. []
  7. IEEE IGARSS: C. Pelletier, G. Webb and F. Petitjean, “Deep Learning for the Classification of Sentinel-2 Image Time Series.” []
  8. AIME: H. Ismail Fawaz, G. Forestier, J. Weber, F. Petitjean, L. Idoumghar and P.-A. Muller , “Automatic alignment of surgical videos using kinematic data. []

2018

  1. Machine Learning: F. Petitjean, W. Buntine, G. Webb, N. Zaidi, “Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes.” []
  2. Data Mining and Knowledge Discovery: G. Webb, L. Lee, B. Goethals and F. Petitjean, “Analyzing concept drift and shift from sample data,” in press. []
  3. 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. []
  4. 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. []
  5. 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. []
  6. SIAM SDM: N. Zaidi, F. Petitjean and G. Webb, “Efficient and Effective Accelerated Hierarchical Higher-OrderLogistic Regression for Large Data Quantities.” []
  7. 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.
  8. Behaviormetrika: Joan Capdevila, He Zhao, F. Petitjean and W. Buntine, “Experiments with learning graphical models on text, ” in press.

2017

  1. IEEE ICDM: G. Forestier, F. Petitjean, H.A. Dau, G. Webb, and E. Keogh, “Generating synthetic time series to augment sparse datasets.” []
  2. SIAM SDM: C. Tan, G. Webb and F. Petitjean, “Indexing and classifying gigabytes of time series under time warping.” []
  3. Machine Learning: N. Zaidi, G. Webb, M. Carman and F. Petitjean, “Efficient Parameter Learning of Bayesian Network Classifiers.” []
  4. 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.”
  5. 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.”
  6. AIME: G. Forestier, F. Petitjean, L. Riffaud & P. Jannin, “Discovering Discriminative and Interpretable Patterns for Surgical Motion Analysis.”
  7. Artificial Intelligence in Medicine: G. Forestier, F. Petitjean, L. Riffaud & P. Jannin, “Automatic matching of surgeries to predict surgeons' next actions.”
  8. 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

  1. Machine Learning: N. Zaidi, G. Webb, M. Carman and F. Petitjean, “ALRn: Accelerated Higher-Order Logistic Regression.”
  2. KDD: G. Webb and F. Petitjean, “A multiple test correction for streams and cascades of statistical hypothesis test.”
  3. Data Mining and Knowledge Discovery: F. Petitjean, T. Li, N. Tatti and G. Webb, “Skopus: Mining top-k sequential patterns under Leverage.”
  4. Data Mining and Knowledge Discovery: G. Webb, R. Hyde, H. Cao, H. Nguyen and F. Petitjean, “Characterizing Concept Drift.”
  5. PAKDD: N. Zaidi, F. Petitjean and G. Webb, “Pre-conditioning an Artificial Neural Network using Naive Bayes,” 2016.
  6. 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

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

2014

  1. 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%)
  2. 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%)
  3. Artificial Intelligence in Medicine: G. Forestier, F. Petitjean, L. Riffaud & P. Jannin, “Non-linear temporal scaling of surgical processes,” 2014.
  4. IEEE Geoscience and Remote Sensing Letters: F. Petitjean & J. Weber, “Efficient satellite image time series analysis under time warping,” 2014. []
  5. 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. []

2013

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

2012

  1. Pattern Recognition Letters: F. Petitjean, C. Kurtz, N. Passat & P. Gançarski, “Spatio-Temporal Reasoning for the Classification of Satellite Image Time Series,” 2012. []
  2. IEEE Transactions on Geoscience and Remote Sensing: F. Petitjean, J. Inglada & P. Gançarski, “Satellite Image Time Series Analysis under Time Warping,” 2012. []
  3. IEEE IGARSS: F. Petitjean, J. Inglada & P. Gançarski, “Introducing prior knowledge in temporal distances for Satellite Image Time Series analysis.”
  4. IEEE IGARSS: F. Petitjean, A. Puissant & P. Gançarski, “Monitoring urban sprawl from Satellite Image Time Series.”
  5. IEEE IGARSS: J. Weber, F. Petitjean & P. Gançarski, “Towards efficient satellite image time series analysis: combination of Dynamic Time Warping and Quasi-Flat Zones.”
  6. Theoretical Computer Science: F. Petitjean & P. Gançarski, “Summarizing a Set of Time Series by Averaging:
    from Steiner Sequence to Compact Multiple Alignment,” 2012.

2011

  1. IEEE Multi-Temp: F. Petitjean, J. Inglada & P. Gançarski, “Clustering of satellite image time series under time warping.”
  2. IEEE IGARSS: F. Petitjean, J. Inglada & P. Gançarski, “Temporal Domain Adaptation under Time Warping.”
  3. IEEE IGARSS: C. Kurtz, F. Petitjean & P. Gançarski, “A context-based approach for the classification of Satellite Image Time Series.”
  4. International Journal of Neural Systems: F. Petitjean F. Masseglia, P. Gançarski & G. Forestier, “Discovering Significant Evolution Patterns from Satellite Image Time Series.”
  5. Pattern Recognition: F. Petitjean, A. Ketterlin & P. Gançarski, “A global averaging method for dynamic time warping, with applications to clustering.” Ranked 8th hottest article in Elsevier's Computer Science journals.
  6. 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.”

Popular press

Program committees / Refereeing

Journals

Conferences / Workshops