Timezone: »
Many complex time series can be effectively subdivided into distinct regimes that exhibit persistent dynamics. Discovering the switching behavior and the statistical patterns in these regimes is important for understanding the underlying dynamical system. We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent switching dynamics. State-dependent switching is enabled by a recurrent state-to-switch connection and an explicit duration count variable is used to improve the time-dependent switching behavior. We demonstrate how to perform efficient inference using a hybrid algorithm that approximates the posterior of the continuous states via an inference network and performs exact inference for the discrete switches and counts. The model is trained by maximizing a Monte Carlo lower bound of the marginal log-likelihood that can be computed efficiently as a byproduct of the inference routine. Empirical results on multiple datasets demonstrate that RED-SDS achieves considerable improvement in time series segmentation and competitive forecasting performance against the state of the art.
Author Information
Abdul Fatir Ansari (National University of Singapore)
Konstantinos Benidis (Amazon Research)
Richard Kurle (AWS AI Labs)
Ali Caner Turkmen (Bogazici University)
Harold Soh (National University of Singapore (NUS))
Alexander Smola (Amazon)
**AWS Machine Learning**
Bernie Wang (AWS AI Labs)
Tim Januschowski (Amazon Research)
- Director Pricing Platform, Zalando SE - Head of Time Series ML at AWS AI
More from the Same Authors
-
2021 Spotlight: Mixture Proportion Estimation and PU Learning:A Modern Approach »
Saurabh Garg · Yifan Wu · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2021 : Benchmarking Multimodal AutoML for Tabular Data with Text Fields »
Xingjian Shi · Jonas Mueller · Nick Erickson · Mu Li · Alexander Smola -
2021 : Modeling Advection on Directed Graphs using Mat\'{e}rn Gaussian Processes for Traffic Flow »
Nadim Saad · Danielle Maddix · Bernie Wang -
2021 : On Symmetries in Variational Bayesian Neural Nets »
Richard Kurle · Tim Januschowski · Jan Gasthaus · Bernie Wang -
2022 : RLSBench: A Large-Scale Empirical Study of Domain Adaptation Under Relaxed Label Shift »
Saurabh Garg · Nick Erickson · James Sharpnack · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2023 Poster: Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting »
Marcel Kollovieh · Abdul Fatir Ansari · Michael Bohlke-Schneider · Jasper Zschiegner · Hao Wang · Yuyang (Bernie) Wang -
2023 Poster: Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition »
Shuhuai Ren · Aston Zhang · Yi Zhu · Shuai Zhang · Shuai Zheng · Mu Li · Alexander Smola · Xu Sun -
2023 Poster: Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models »
Alvin Heng · Harold Soh -
2023 Poster: The Best of Both Worlds in Network Population Games: Reaching Consensus and Convergence to Equilibrium »
Shuyue Hu · Harold Soh · Georgios Piliouras -
2022 : Trustworthy Robots for Human-Robot Interaction »
Harold Soh -
2022 Poster: Adaptive Interest for Emphatic Reinforcement Learning »
Martin Klissarov · Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Taesup Kim · Alexander Smola -
2022 Poster: Faster Deep Reinforcement Learning with Slower Online Network »
Kavosh Asadi · Rasool Fakoor · Omer Gottesman · Taesup Kim · Michael Littman · Alexander Smola -
2022 Poster: Graph Reordering for Cache-Efficient Near Neighbor Search »
Benjamin Coleman · Santiago Segarra · Alexander Smola · Anshumali Shrivastava -
2022 Poster: On the detrimental effect of invariances in the likelihood for variational inference »
Richard Kurle · Ralf Herbrich · Tim Januschowski · Yuyang (Bernie) Wang · Jan Gasthaus -
2021 : Backpropagation through Back substitution with a Backslash »
Ekin Akyürek · Alan Edelman · Bernie Wang -
2021 Poster: Neural Flows: Efficient Alternative to Neural ODEs »
Marin Biloš · Johanna Sommer · Syama Sundar Rangapuram · Tim Januschowski · Stephan Günnemann -
2021 Poster: Detecting Anomalous Event Sequences with Temporal Point Processes »
Oleksandr Shchur · Ali Caner Turkmen · Tim Januschowski · Jan Gasthaus · Stephan Günnemann -
2021 Poster: Mixture Proportion Estimation and PU Learning:A Modern Approach »
Saurabh Garg · Yifan Wu · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2021 Poster: Probabilistic Forecasting: A Level-Set Approach »
Hilaf Hasson · Bernie Wang · Tim Januschowski · Jan Gasthaus -
2021 Poster: Online false discovery rate control for anomaly detection in time series »
Quentin Rebjock · Baris Kurt · Tim Januschowski · Laurent Callot -
2021 Poster: Latent Matters: Learning Deep State-Space Models »
Alexej Klushyn · Richard Kurle · Maximilian Soelch · Botond Cseke · Patrick van der Smagt -
2021 Poster: Continuous Doubly Constrained Batch Reinforcement Learning »
Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Pratik Chaudhari · Alexander Smola -
2020 Poster: Deep Rao-Blackwellised Particle Filters for Time Series Forecasting »
Richard Kurle · Syama Sundar Rangapuram · Emmanuel de Bézenac · Stephan Günnemann · Jan Gasthaus -
2020 Poster: Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation »
Rasool Fakoor · Jonas Mueller · Nick Erickson · Pratik Chaudhari · Alexander Smola -
2020 Poster: Normalizing Kalman Filters for Multivariate Time Series Analysis »
Emmanuel de Bézenac · Syama Sundar Rangapuram · Konstantinos Benidis · Michael Bohlke-Schneider · Richard Kurle · Lorenzo Stella · Hilaf Hasson · Patrick Gallinari · Tim Januschowski -
2019 : Poster Session »
Ayse Cakmak · Yunkai Zhang · Srijith Prabhakarannair Kusumam · Mohamed Osama Ahmed · Xintao Wu · Jayesh Choudhari · David I Inouye · Thomas Taylor · Michel Besserve · Ali Caner Turkmen · Kazi Islam · Antonio Artés · Amrith Setlur · Zhanghua Fu · Zhen Han · Abir De · Nan Du · Pablo Sanchez-Martin -
2019 : Invited Talk - Alexander J. Smola - Sets and symmetries »
Alexander Smola -
2019 : Intermittent Demand Forecasting with Deep RenewalProcesses »
Ali Caner Turkmen -
2019 Poster: Embedding Symbolic Knowledge into Deep Networks »
Yaqi Xie · Ziwei Xu · Kuldeep S Meel · Mohan Kankanhalli · Harold Soh -
2019 Poster: Learning Hierarchical Priors in VAEs »
Alexej Klushyn · Nutan Chen · Richard Kurle · Botond Cseke · Patrick van der Smagt -
2019 Spotlight: Learning Hierarchical Priors in VAEs »
Alexej Klushyn · Nutan Chen · Richard Kurle · Botond Cseke · Patrick van der Smagt -
2018 Poster: Deep State Space Models for Time Series Forecasting »
Syama Sundar Rangapuram · Matthias W Seeger · Jan Gasthaus · Lorenzo Stella · Bernie Wang · Tim Januschowski -
2017 : TBA11 »
Alexander Smola -
2017 Oral: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2016 Poster: Variance Reduction in Stochastic Gradient Langevin Dynamics »
Kumar Avinava Dubey · Sashank J. Reddi · Sinead Williamson · Barnabas Poczos · Alexander Smola · Eric Xing -
2016 Poster: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization »
Sashank J. Reddi · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2015 : Scaling Machine Learning »
Alexander Smola -
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing -
2015 Poster: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Spotlight: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Poster: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants »
Sashank J. Reddi · Ahmed Hefny · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2014 Poster: Communication Efficient Distributed Machine Learning with the Parameter Server »
Mu Li · David G Andersen · Alexander Smola · Kai Yu -
2014 Poster: Spectral Methods for Indian Buffet Process Inference »
Hsiao-Yu Tung · Alexander Smola -
2013 Workshop: Topic Models: Computation, Application, and Evaluation »
David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar -
2013 Workshop: Randomized Methods for Machine Learning »
David Lopez-Paz · Quoc V Le · Alexander Smola -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2013 Poster: Variance Reduction for Stochastic Gradient Optimization »
Chong Wang · Xi Chen · Alexander Smola · Eric Xing -
2012 Workshop: Confluence between Kernel Methods and Graphical Models »
Le Song · Arthur Gretton · Alexander Smola -
2012 Session: Oral Session 10 »
Alexander Smola -
2012 Poster: Learning Networks of Heterogeneous Influence »
Nan Du · Le Song · Alexander Smola · Ming Yuan -
2012 Poster: FastEx: Fast Clustering with Exponential Families »
Amr Ahmed · Sujith Ravi · Shravan M Narayanamurthy · Alexander Smola -
2012 Spotlight: Learning Networks of Heterogeneous Influence »
Nan Du · Le Song · Alexander Smola · Ming Yuan -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Tutorial: Graphical Models for the Internet »
Amr Ahmed · Alexander Smola -
2010 Workshop: Challenges of Data Visualization »
Barbara Hammer · Laurens van der Maaten · Fei Sha · Alexander Smola -
2010 Poster: Word Features for Latent Dirichlet Allocation »
James Petterson · Alexander Smola · Tiberio Caetano · Wray L Buntine · Shravan M Narayanamurthy -
2010 Poster: Optimal Web-Scale Tiering as a Flow Problem »
Gilbert Leung · Novi Quadrianto · Alexander Smola · Kostas Tsioutsiouliklis -
2010 Poster: Multitask Learning without Label Correspondences »
Novi Quadrianto · Alexander Smola · Tiberio Caetano · S.V.N. Vishwanathan · James Petterson -
2010 Poster: Parallelized Stochastic Gradient Descent »
Martin A Zinkevich · Markus Weimer · Alexander Smola · Lihong Li -
2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets »
Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin -
2009 Poster: Slow Learners are Fast »
Martin A Zinkevich · Alexander Smola · John Langford -
2009 Poster: Distribution Matching for Transduction »
Novi Quadrianto · James Petterson · Alexander Smola -
2008 Poster: Kernelized Sorting »
Novi Quadrianto · Le Song · Alexander Smola -
2008 Poster: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Spotlight: Kernelized Sorting »
Novi Quadrianto · Le Song · Alexander Smola -
2008 Spotlight: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Poster: Tighter Bounds for Structured Estimation »
Olivier Chapelle · Chuong B Do · Quoc V Le · Alexander Smola · Choon Hui Teo -
2008 Poster: Robust Near-Isometric Matching via Structured Learning of Graphical Models »
Julian J McAuley · Tiberio Caetano · Alexander Smola -
2007 Workshop: Representations and Inference on Probability Distributions »
Kenji Fukumizu · Arthur Gretton · Alexander Smola -
2007 Poster: Convex Learning with Invariances »
Choon Hui Teo · Amir Globerson · Sam T Roweis · Alexander Smola -
2007 Spotlight: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Spotlight: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Poster: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking »
Markus Weimer · Alexandros Karatzoglou · Quoc V Le · Alexander Smola -
2007 Oral: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Poster: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Spotlight: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking »
Markus Weimer · Alexandros Karatzoglou · Quoc V Le · Alexander Smola -
2007 Demonstration: Elefant »
Kishor Gawande · Alexander Smola · Vishwanathan S V N · Li Cheng · Simon A Guenter -
2007 Spotlight: Convex Learning with Invariances »
Choon Hui Teo · Amir Globerson · Sam T Roweis · Alexander Smola -
2006 Poster: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Spotlight: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Talk: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola