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:
08:00-08:10 Opening remarks
08:10-09:30 Contributed Talk
09:30-10:00 Coffee Break
10:00-11:00 Keynote Talk
11:00-12:00 Keynote Talk
12:00-13:00 Lunch Break
13:00-14:00 Keynote Talk
14:00-14:30 Poster Highlights
14:30-16:00 Poster Session & Coffee Break
16:00-16:45 Contributed Talks
16:45-17:00 Concluding Remarks
University of Washington
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.
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.
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.
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
Heterogeneous Relational Kernel Learning Andre Nguyen and Edward Raff
Deep Learning Models for Predicting CO2 Flux Employing Multivariate Time Series Phuong Nguyen and Milton Halem
Bidirectional Imputation of Sensor-based Time Series Data Yuhui Wang and Diane Cook
DenseNets for Time Series Classification: towards automation of time series pre-processing with CNNs Guillaume Richard, Georges Hébrail, Mathilde Mougeot and Nicolas Vayatis
Event Characterization and Separation using Wavelet Signatures Amrita Anam, Aryya Gangopadhyay and Nirmalya Roy
Contextual Anomaly Detection by Correlated Probability Distributions using Kullback-Leibler Divergence Jinwoo Cho, Shahroz Tariq, Sangyup Lee, Young Geun Kim, Jeong-Han Yun, Jonguk Kim, Hyoung Chun Kim and Simon Woo
Increasing Lead Time and Granularity of Civil Unrest Prediction through Time Series Data Lu Meng and Rohini Srihari
Multi-Domain Anomalous Temporal Association (Multi-DATA) -- Moving towards explainability from multiple notions of time Vandana Janeja and Suraksha Shukla
MLAT: Metric Learning for kNN in Streaming Time Series Dongmin Park, Susik Yoon, Hwanjun Song and Jae-Gil Lee
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: firstname.lastname@example.org.
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