Held in conjunction with KDD'20
Aug 24th, 2020 - San Diego, California, USA
6th Workshop on
Mining and Learning from Time Series

Videos of keynote talks, contributed talks and poster highlights are available at the MiLeTS 2020 YouTube Channel.


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.
  • Time series with special structure: spatiotemporal (e.g., traffic speeds 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 that are multivariate, high-dimensional, heterogeneous, etc., or that possess other atypical properties.
  • Time series analysis using less traditional approaches, such as deep learning and subspace clustering.
  • Privacy preserving time series mining and learning.
  • Online, high-speed learning and mining from streaming time series.
  • Uncertain time series mining.
  • 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.


8:00 AM - 5:00 PM, August 24th, 2020



08:00-08:10 Opening remarks

08:10-09:10 Keynote Talk

  • Machine Learning for Healthcare in the COVID-19 Era, Mihaela van der Schaar

09:15-10:00 Keynote Talk

  • Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting, Inderjit S. Dhillon

10:00-10:30 Coffee Break

10:30-12:00 Contributed Talks

  • The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models. Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas and Jan Gasthaus
  • Driver2vec: Driver Identification from Automotive Data. Jingbo Yang, Ruge Zhao, Meixian Zhu, Jaka Sodnik, David Hallac and Jure Leskovec
  • Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection. Farzaneh Khoshnevisan, Zhewen Fan and Vitor Carvalho
  • Detection of Environment Transitions in Time Series Data for Responsive Science. Ameya Daigavane, Kiri Wagstaff, Gary Doran, Corey Cochrane, Caitriona Jackman and Abigail Rymer
  • Instance Explainable Temporal Network For Multivariate Timeseries. Naveen Madiraju and Homa Karimabadi

12:00-13:00 Lunch Break


13:00-14:00 Keynote Talk

  • The Efficient Transformer , Łukasz Kaiser

14:00-14:30 Poster Spotlights

14:30-15:00 Coffee Break

15:00-16:00 Keynote Talk

  • Machine Learning for Aviation Safety, Nikunj Oza

16:00-16:45 Poster Session

16:45-17:00 Concluding Remarks



Inderjit S. Dhillon

Inderjit S. Dhillon

Professor, Amazon Fellow
University of Texas at Austin & Amazon

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions. Modern datasets can have millions of correlated time-series that evolve together, i.e they are extremely high dimensional (one dimension for each individual time-series). There is a need for exploiting global patterns and coupling them with local calibration for better prediction. However, most recent deep learning approaches in the literature are one-dimensional, i.e, even though they are trained on the whole dataset, during prediction, the future forecast for a single dimension mainly depends on past values from the same dimension. In this paper, we seek to correct this deficiency and propose DeepGLO, a deep forecasting model which thinks globally and acts locally. In particular, DeepGLO is a hybrid model that combines a global matrix factorization model regularized by a temporal convolution network, along with another temporal network that can capture local properties of each time-series and associated covariates. Our model can be trained effectively on high-dimensional but diverse time series, where different time series can have vastly different scales, without a priori normalization or rescaling. Empirical results demonstrate that DeepGLO can outperform state-of-the-art approaches; for example, we see more than 25% improvement in WAPE over other methods on a public dataset that contains more than 100K-dimensional time series. This is joint work with Rajat Sen and Hsiang-Fu Yu.

Inderjit Dhillon is the Gottesman Family Centennial Professor of Computer Science and Mathematics at UT Austin, where he is also the Director of the ICES Center for Big Data Analytics. Currently he is on leave from UT Austin and heads the Amazon Research Lab in Berkeley, California, where he is developing and deploying state-of-the-art machine learning methods for Amazon Search. His main research interests are in big data, machine learning, network analysis, linear algebra and optimization. He received his B.Tech. degree from IIT Bombay, and Ph.D. from UC Berkeley. Inderjit has received the following awards: the ICES Distinguished Research Award, the SIAM Outstanding Paper Prize, the Moncrief Grand Challenge Award, the SIAM Linear Algebra Prize, the University Research Excellence Award, and the NSF Career Award. He has published over 175 journal and conference papers, and has served on the Editorial Board of the Journal of Machine Learning Research, the IEEE Transactions of Pattern Analysis and Machine Intelligence, Foundations and Trends in Machine Learning and the SIAM Journal for Matrix Analysis and Applications. Inderjit is actively involved with industry. He is currently an Amazon Fellow at A9/Amazon, where he is developing and deploying state-of-the-art machine learning methods in Amazon search. Prior to joining Amazon, Inderjit worked as a quantitative analyst at a hedge fund, Voleon, which does systematic machine learning statistical arbitrage. In the past, Inderjit has been a consultant for Walmart Labs, Sabre Inc, Yahoo!, Syncata, and Neonyoyo. Inderjit is an ACM Fellow, an IEEE Fellow, a SIAM Fellow and an AAAS Fellow.
Łukasz Kaiser

Łukasz Kaiser

Staff Research Scientist
Google Brain & CNRS

Reformer: The Efficient Transformer

Transformer models have been used in a variety of fields and yield great results on many NLP tasks. But between the BERT, GPT-2, and many other variants, they can be inefficient and it can be hard to apply them. I will introduce a new efficient variant of Transformer called the Reformer. I'll take you through the code that implements it and I will show how it runs at high efficiency and addresses the main problems or high memory use and low performance on long sequences that limited the use of some Transformers before. I will finish with new applications of Reformer that open up.

Łukasz Kaiser joined Google in 2013 and is currently a staff Research Scientist in the Google Brain Team in Mountain View, where he works on fundamental aspects of deep learning and natural language processing. He has co-designed state-of-the-art neural models for machine translation, parsing and other algorithmic and generative tasks and co-authored the TensorFlow system, the Tensor2Tensor and Trax libraries and the Transformer model. Before joining Google, Lukasz was a tenured researcher at University Paris Diderot and worked on logic and automata theory. He received his PhD from RWTH Aachen University in 2008 and his MSc from the University of Wroclaw, Poland.

Mihaela van der Schaar

Mihaela van der Schaar

University of Cambridge, UCLA, The Alan Turing Institute

Machine Learning for Healthcare in the COVID-19 Era

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA. Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards. In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI. Mihaela’s current research focus is on machine learning, AI and operations research for healthcare and medicine.

Nikunj Oza

Nikunj Oza

Leader of Data Sciences Group
NASA Ames Research Center

Machine Learning for Aviation Safety

The National Airspace, with all of its aircraft, airports, personnel, and related infrastructure, is an incredibly safe system. It is essential that we keep it safe through the changes that it is experiencing, including expected increases in traffic over time, increasing variety of traffic in the form of Unmanned Aerial Vehicles (UAVs), and sharp reductions and increases in traffic due to transient phenomena such as pandemics. The system is currently monitored for safety issues through a set of exceedances, which are rules describing various known safety issues. By definition, these rules cannot identify previously-unknown safety issues. Additionally, they do not identify precursors to these safety issues—states that may not represent safety issues by themselves, but are circumstances under which safety issues are more likely to occur in the near future. In this talk, I describe machine learning-based methods that we have developed for anomaly detection and precursor identification and the aviation safety results that we have obtained. I also describe the active learning algorithm that we have developed to mitigate the false alarm problem that is common to data-driven anomaly detection methods. Our ultimate aim is to allow for aviation safety analysts to discover new safety issues as they arise.

Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team, which applies machine learning to aviation safety and operations problems. Dr. Oza’s 50+ research papers represent his research interests, which include data mining, machine learning, ensemble learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2018 and 2019 NASA Honor Awards and the 2010 NASA Aeronautics Research Mission Directorate Associate Administrator¹s Award. In 2019, he was named by Cognilytica as one of 50 key people in the US government working to move the adoption of AI forward across the industry. He is an Associate Editor for the peer-reviewed journal Information Fusion (Elsevier) and has served as organizer, senior program committee member, and program committee member of several data mining and machine learning conferences. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.

Accepted Papers


The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas and Jan Gasthaus. [Video]

Driver2vec: Driver Identification from Automotive Data Jingbo Yang, Ruge Zhao, Meixian Zhu, Jaka Sodnik, David Hallac and Jure Leskovec. [Video]

Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection Farzaneh Khoshnevisan, Zhewen Fan and Vitor Carvalho. [Video]

Detection of Environment Transitions in Time Series Data for Responsive Science Ameya Daigavane, Kiri Wagstaff, Gary Doran, Corey Cochrane, Caitriona Jackman and Abigail Rymer. [Video]

Instance Explainable Temporal Network For Multivariate Timeseries Naveen Madiraju and Homa Karimabadi. [Video]


Accepted Posters


Modality Selection for Classification on Time-series Data Murchana Baruah and Bonny Banerjee. [Video]

Machine Learning Methods for Predictive Maintenance of Multifunctional Printers Wojciech Indyk, Zuzana Neverilova and Jakub Valcik. [Video]

Towards Deep Unsupervised Representation Learning from Accelerometer Time Series for Animal Activity Recognition Jacob Kamminga, Viet Duc Le, Nirvana Meratnia and Paul Havinga. [Video]

Statistical Evaluation of Anomaly Detectors for Sequences Erik Scharwächter and Emmanuel Müller. [Video]

Towards a framework for incorporating data acquisition cost in predictive time series models Adrian Stetco, Razvan Mosincat, Goran Nenadic and John Keane. [Video]

PastProp-RNN: improved predictions of the future by correcting the past André Baptista, Yassine Baghoussi, Carlos Soares, Miguel Arantes and João Mendes-Moreira. [Video]

RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun and Huan Xu. [Video]

Forecasting Hierarchical Time Series with a Regularized Embedding Space Jeffrey Gleason. [Video]

Towards Automating Time Series Analysis for Paleogeosciences Deborah Khider, Pratheek Athreya, Varun Ratnakar, Yolanda Gil, Feng Zhu, Myron Kwan and Julien Emile-Geay. [Video]

Interpreting Deep Temporal Neural Networks by Selective Visualization of Internally Activated Nodes Sohee Cho, Wonjoon Chang, Ginkyeng Lee and Jaesik Choi. [Video]


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 theAll submissions must be in pdf format using the workshop template ( latex, word). Submissions will be managed via the MiLeTS 2020 EasyChair website: https://easychair.org/conferences/?conf=milets2020.

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.

COVID-19 Time Series Analysis Special Track: The COVID-19 pandemic is impacting almost everyone worldwide and is expected to have life-altering short and long-term effects. There are many potential applications of time series analysis and mining that can contribute to understanding of this pandemic. We encourage submission of high quality manuscripts describing original problems, time series datasets, and novel solutions for time series analysis and forecasting of COVID-19.

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: June 10th June 20th, 2020, 11:59PM Alofi Time

Author Notification: July 13th, 2020

Camera Ready Version: July 27th, 2020

Video Submission: July 30th, 2020

Workshop: August 24th, 2020

Workshop Organizers & Steering Committee


Sanjay Purushotham

University of Maryland, Baltimore County


Qi (Rose) Yu

University of California, San Diego


YaGuang Li



Eamonn Keogh

University of California Riverside


Yan Liu

University of Southern California


Abdullah Mueen

University of New Mexico


Program Committee

  • Zhengping Che, Didi Chuxing
  • Dehua Cheng, Facebook AI
  • Jing Dai, Google
  • Abhishek Mukherji, Cisco Systems
  • Sungyong Seo, University of Southern California
  • Xingjian Shi, Amazon Web Sevices
  • Michael Tsang, University of Southern California
  • Zheng Wang, Didi Chuxing
  • Qingsong Wen, Alibaba DAMO Academy
  • Bin Yang, Aalborg University
  • Scott Yang, New York University
  • Michael Yeh, Visa Research
  • Jiayu Zhou, Michigan State University