Held in conjunction with KDD'19
Aug 5, 2019 - Anchorage, Alaska, USA
5th Workshop on
Mining and Learning from Time Series

Introduction

Time series data are ubiquitous. In domains as diverse as finance, entertainment, transportation and health care, we observe a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. Rapid advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating a rapid growth in the size and complexity of time series archives. Thus, although time series analysis has been studied extensively, its importance only continues to grow. What is more, modern time series data pose significant challenges to existing techniques (e.g., irregular sampling in hospital records and spatiotemporal structure in climate data). Finally, time series mining research is challenging and rewarding because it bridges a variety of disciplines and demands interdisciplinary solutions. Now is the time to discuss the next generation of temporal mining algorithms. The focus of MiLeTS workshop is to synergize the research in this area and discuss both new and open problems in time series analysis and mining. The solutions to these problems may be algorithmic, theoretical, statistical, or systems-based in nature. Further, MiLeTS emphasizes applications to high impact or relatively new domains, including but not limited to biology, health and medicine, climate and weather, road traffic, astronomy, and energy.
The MiLeTS workshop will discuss a broad variety of topics related to time series, including:

  • Time series pattern mining and detection, representation, searching and indexing, classification, clustering, prediction, forecasting, and rule mining.
  • BIG time series data.
  • Hardware acceleration techniques using GPUs, FPGAs and special processors.
  • Online, high-speed learning and mining from streaming time series.
  • Uncertain time series mining.
  • Privacy preserving time series mining and learning.
  • Time series that are multivariate, high-dimensional, heterogeneous, etc., or that possess other atypical properties.
  • Time series with special structure: spatiotemporal (e.g., wind patterns at different locations), relational (e.g., patients with similar diseases), hierarchical, etc.
  • Time series with sparse or irregular sampling, non-random missing values, and special types of measurement noise or bias.
  • Time series analysis using less traditional approaches, such as deep learning and subspace clustering.
  • Applications to high impact or relatively new time series domains, such as health and medicine, road traffic, and air quality.
  • New, open, or unsolved problems in time series analysis and mining.

Schedule

Tubughnenq 3- Level 2, Dena'ina
8:00 AM - 5:00 PM
August 5th, 2019

 

MORNING SESSION

08:00-08:10 Opening remarks

08:10-09:30 Contributed Talk

  • MASA: Motif-Aware State Assignment in Noisy Time Series Data , Saachi Jain, David Hallac, Rok Sosic and Jure Leskovec
  • Classifying humans using Deep time-series transfer learning : accelerometric gait-cycles to gyroscopic squats , Vinay Prabhu, Stephanie Tietz and Anh Ta
  • Metadata-Augmented Neural Networks for Cross-Location Solar Irradiation Prediction from Satellite Images , Kuan-Ying Lee, Hsin-Fu Huang, Hung-Yueh Chiang, Hu-Cheng Lee, Winston Hsu and Wen-Chin Chen
  • A Formally Robust Time Series Distance Metric , Maximilian Toller, Bernhard Geiger and Roman Kern
  • Enumerating Hub Motifs in Time Series Based on the Matrix Profile , Genta Yoshimura, Atsunori Kanemura and Hideki Asoh

09:30-10:00 Coffee Break

10:00-11:00 Keynote Talk

  • Flexibility, Interpretability, and Scalability in Time Series Modeling , Emily Fox

11:00-12:00 Keynote Talk

  • AI for Transportation , Jieping Ye

12:00-13:00 Lunch Break

AFTERNOON SESSION

13:00-14:00 Keynote Talk

  • Time Series Analysis for Massive Sensor Network Data from cars, airplanes and smart buildings , Jure Leskovec

14:00-14:30 Poster Highlights

14:30-16:00 Poster Session & Coffee Break

16:00-16:45 Contributed Talks

  • Online FDR Controlled Anomaly Detection for Streaming Time Series, Weinan Wang, Zhengyi Liu, Lucas Pierce and Xiaolin Shi
  • Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting, Razvan-Gabriel Cirstea, Chenjuan Guo and Bin Yang
  • Probabilistic Forecasting with Temporal Convolutional Neural Network, Yitian Chen, Yanfei Kang, Yixiong Chen and Zizhuo Wang

16:45-17:00 Concluding Remarks

 

Speakers

Emily Fox

Emily Fox

Associate Professor
University of Washington

Flexibility, Interpretability, and Scalability in Time Series Modeling

We are increasingly faced with the need to analyze complex data streams; for example, sensor measurements from wearable devices. Machine learning—and moreover deep learning—has brought many recent success stories to the analysis of complex sequential data sources, including speech, text, and video. However, these success stories involve a clear prediction goal combined with a massive (benchmark) training dataset. Unfortunately, many real-world tasks go beyond simple predictions, especially in cases where models are being used as part of a human decision-making process. For example, imagine the challenge of forecasting metro-level homeless populations based on historical annual single-night counts, or inferring the structure of gene regulatory networks from limited observations of their complex non-linear dynamics. Such complex scenarios necessitate notions of interpretability and measures of uncertainty. Furthermore, in aggregate the datasets might be large, but we might have limited data for an individual stream, requiring parsimonious modeling approaches.

In this talk, we first discuss how sparsity-inducing penalties can be deployed on the weights of deep neural networks to enable interpretable structure learning, in addition to yielding more parsimonious models that better handle limited data scenarios. We then turn to Bayesian dynamical modeling of individually sparse data streams, flexibly sharing information, accounting for uncertainty, and handling non-stationarities. Finally, we discuss our recent body of work on scaling learning in sequential data scenarios by considering stochastic gradient based approaches and mitigating the bias introduced in subsampling dependent data. We explore these ideas within the context of Markov chain Monte Carlo methods and training recurrent neural networks (RNNs). Throughout the talk, we provide analyses of neuroimaging, genomic, housing and homelessness data sources, and a language modeling task.


Bio
Dr. Emily Fox is a Principal Research Scientist at Apple and an Associate Professor in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington, where she holds the Amazon Professorship of Machine Learning. She received her Ph.D. in EECS from MIT, with her dissertation being awarded the Leonard J. Savage Thesis Award in Applied Methodology and MIT EECS Jin-Au Kong Outstanding Doctoral Thesis Prize. She has also been awarded a Presidential Early Career Award for Scientists and Engineers (2017), Sloan Research Fellowship (2015), ONR Young Investigator award (2015), NSF CAREER award (2014), Her research interests are in large-scale dynamic modeling and computations, with a focus on Bayesian methods.


Jieping Ye

Jieping Ye

Vice President
Didi Chuxing

AI for Transportation

Didi Chuxing is the world’s leading mobile transportation platform that offers a full range of app-based transportation options for 550 million users. Every day, DiDi's platform receives over 100TB new data, processes more than 40 billion routing requests, and acquires over 15 billion location points. In this talk, I will show how AI technologies have been applied to analyze such big transportation data to improve the travel experience for millions of users.


Bio
Dr. Jieping Ye is head of Didi AI Labs and a VP of Didi Chuxing. He is also a professor of University of Michigan, Ann Arbor. His research interests include big data, machine learning, and data mining with applications in transportation and biomedicine. He has served as a Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, ICML, KDD, IJCAI, ICDM, and SDM. He has served as an Associate Editor of Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at ICML in 2004, the KDD best research paper runner up in 2013, and the KDD best student paper award in 2014.


Jure Leskovec

Jure Leskovec

Associate Professor
Stanford University

Time Series Analysis for Massive Sensor Network Data from cars, airplanes and smart buildings [Slides]


Bio
Dr. Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His research focuses on machine learning and data mining applied to social, information and biological networks, their evolution, and the diffusion of information and influence over them. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, economics, marketing, and healthcare. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University.

Accepted Papers

 

Metadata-Augmented Neural Networks for Cross-Location Solar Irradiation Prediction from Satellite Images Kuan-Ying Lee, Hsin-Fu Huang, Hung-Yueh Chiang, Hu-Cheng Lee, Winston Hsu and Wen-Chin Chen

Probabilistic Forecasting with Temporal Convolutional Neural Network Yitian Chen, Yanfei Kang, Yixiong Chen and Zizhuo Wang

MASA: Motif-Aware State Assignment in Noisy Time Series Data Saachi Jain, David Hallac, Rok Sosic and Jure Leskovec

A Formally Robust Time Series Distance Metric Maximilian Toller, Bernhard Geiger and Roman Kern

Enumerating Hub Motifs in Time Series Based on the Matrix Profile Genta Yoshimura, Atsunori Kanemura and Hideki Asoh

Online FDR Controlled Anomaly Detection for Streaming Time Series Weinan Wang, Zhengyi Liu, Lucas Pierce and Xiaolin Shi

Classifying humans using Deep time-series transfer learning : accelerometric gait-cycles to gyroscopic squats Vinay Prabhu, Stephanie Tietz and Anh Ta

Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting Razvan-Gabriel Cirstea, Chenjuan Guo and Bin Yang

 

Accepted Posters

Call for Papers

Submissions should follow the SIGKDD formatting requirements and will be evaluated using the SIGKDD Research Track evaluation criteria. Preference will be given to papers that are reproducible, and authors are encouraged to share their data and code publicly whenever possible. Submissions are strongly recommended to be no more than 4 pages, excluding references or supplementary materials (all in a single pdf). The appropriateness of using additional pages over the recommended length will be judged by reviewers. All submissions must be in pdf format using the workshop template ( latex, word). Submissions will be managed via the MiLeTS 2019 EasyChair website.

Note on open problem submissions: In order to promote new and innovative research on time series, we plan to accept a small number of high quality manuscripts describing open problems in time series analysis and mining. Such papers should provide a clear, detailed description and analysis of a new or open problem that poses a significant challenge to existing techniques, as well as a thorough empirical investigation demonstrating that current methods are insufficient.

The review process is single-round and double-blind (submission files have to be anonymized). Concurrent submissions to other journals and conferences are acceptable. Accepted papers will be presented as posters during the workshop and list on the website. Besides, a small number of accepted papers will be selected to be presented as contributed talks.

Any questions may be directed to the workshop e-mail address: kdd.milets@gmail.com.

Key Dates

 

Paper Submission Deadline: May 5th, 2019 May 12th, 2019 11:59PM Alofi Time

Author Notification: June 1st, 2019 June 8th, 2019

Camera Ready Version: June 22nd, 2019

Workshop: August 5th, 2019

Workshop Organizers

 

Zheng Wang

DiDi Labs

 

Sanjay Purushotham

University of Maryland, Baltimore County

 

Yaguang Li

University of Southern California

Steering Committee

 

Eamonn Keogh

University of California Riverside

 

Yan Liu

University of Southern California

 

Abdullah Mueen

University of New Mexico

 

Program Committee

  • Mohammad Taha Bahadori, Amazon
  • Gustavo Batista, UNSW, Sydney
  • Nurjahan Begum, University of California, Riverside
  • Zhengping Che, Didi Chuxing
  • Dingxiong Deng, Facebook
  • Xiang Li, Center for Clinical Data Science, Massachusetts General Hospital
  • Spiros Papadimitriou, Rutgers University
  • Francois Petitjean, Monash University
  • Diego Silva, Universidade Federal de São Carlos
  • Souhaib Ben Taieb, University of Mons
  • Rose Yu, Northeastern University
  • Jiayu Zhou, Michigan State University