Pattern Selection Problems in Multivariate Time-Series using - PowerPoint PPT Presentation

Jul 01, 2023 •495 likes •621 views

Pattern Selection Problems in Multivariate Time-Series using Equation Discovery Arne Koopman, Arno Knobbe, Marving Meeng Leiden University. The university to discover. InfraWatch Data Mining for Infrastructure Asset Management - Hollandse Brug

Pattern Selection Problems in Multivariate Time-Series using Equation Discovery Arne Koopman, Arno Knobbe, Marving Meeng Leiden University. The university to discover.
InfraWatch Data Mining for Infrastructure Asset Management - Hollandse Brug – a Dutch highway bridge - Monitoring of events (i.e. degradation, congestion) Leiden University. The university to discover.
InfraWatch - 145 sensors: continuous time-series data - Various types: geophones, strain sensors, temperature sensors Leiden University. The university to discover.
(Too) Much Sensor Data? - 145 sensors : 145 continuous streams - Sampling at 100 Hz : ~4GB /day - Is all of this useful? - Or… can we select a few sensors that provide a good view on the whole system? Leiden University. The university to discover.
Relevant Sensors - Idea: sensors that have similar sensor readings are assumed redundant - Select a set of non- redundant sensors that provide a overall picture of the complete system Leiden University. The university to discover.
Sets of Sensors = Equation - Sensor x is described by a sensor set - Select a set of sensors that have events that coincide : they describe the same events Leiden University. The university to discover.
Equations - LaGramge’s grammar defines an equation type, - such as linear: - ..or differential: - … or, can use expert knowledge to define known relations between signals Leiden University. The university to discover.
Which Equations? LaGramge - Fit candidate equations to the data - Pick all equations that fit the signal well Selection - Pick equation set that models the system well Leiden University. The university to discover.
Selecting on Quality - Signals in similar range, therefore: do not boost signals too much - Select equations with scalars c close to 1 - 2 greedy search strategies: ascending and descending size ordered candidate equations Leiden University. The university to discover.
Compact Equations - Example - Sensor 100 is explained by sensors that are close by, and have signals that are correlated Leiden University. The university to discover.
Final Remarks Equation Discovery - LaGramge suitable to bridge the gap between continuous data and pattern discovery - Equation sets can be used as a compact description of a continuous system InfraWatch - Visit our website: www.infrawatch.com Leiden University. The university to discover.

Download Presentation

Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Comments on Modelling the business cycle in a multivariate structural time series model for

Comments on Modelling the business cycle in a multivariate structural time series model for the G7 and the Netherlands Gabriel Perez Quiros The usual disclaimer applies Brief summary. Specification Structural time series model that

511 views • 24 slides

Detecting seasonality changes in multivariate extremes of climatological time series Philippe

Detecting seasonality changes in multivariate extremes of climatological time series Philippe Naveau Laboratoire des Sciences du Climat et lEnvironnement, France joint work with Sebastian Engelke (Geneva University) and Chen Zhou (Erasmus

888 views • 71 slides

MULTIVARIATE TIME SERIES & FORECASTING 1 2 Vector ARMA models

MULTIVARIATE TIME SERIES & FORECASTING 1 2 Vector ARMA models E 0 & Cov t t Stationarity if the roots of the equation are all greater than 1 in absolute value Then : infinite MA

201 views • 18 slides

Time Series Representations for Better Data Mining What can we do with time series data?

Time Series Representations for Better Data Mining What can we do with time series data? Clustering Anomaly (outlier) detection Forecasting What are the problems with time series data? Noise Concept-drift (trend-shift

122 views • 11 slides

Modeling business and financial cycle in a multivariate structural time series model Irma

Modeling business and financial cycle in a multivariate structural time series model Irma Hindrayanto DNB Siem Jan Koopman VU / CREATES Jasper de Winter DNB 2 September 2016 Workshop on Estimating and Interpreting Financial Cycles De

900 views • 24 slides

DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting Siteng Huang,

DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting Siteng Huang, Donglin Wang, Xuehan Wu, Ao Tang Presenter: Siteng Huang Machine Intelligence Laboratory, Department of Engineering, Westlake University, Hangzhou, China

361 views • 16 slides

LFZip: Lossy compression of multivariate time series data via improved prediction Shubham

LFZip: Lossy compression of multivariate time series data via improved prediction Shubham Chandak Stanford University DCC 2020 Paper ID: 111 Joint work with Kedar Tatwawadi, Stanford Tsachy Weissman, Stanford Chengtao Wen, Siemens

256 views • 25 slides

Multivariate modelling of time series of infectious disease counts Michaela Paul Leonhard Held

Multivariate modelling of time series of infectious disease counts Michaela Paul Leonhard Held Biostatistics Unit Institute of Social and Preventive Medicine University of Zurich Reisensburg, September 28, 2007 Introduction Modelling

570 views • 30 slides

Bayesian Variable Selection for Nowcasting Economic Time Series Steven L. Scott Hal R. Varian

Bayesian Variable Selection for Nowcasting Economic Time Series Steven L. Scott Hal R. Varian July 2012 THIS DRAFT: August 4, 2014 Abstract We consider the problem of short-term time series forecasting (nowcasting) when there are more

328 views • 22 slides

Modeling, simulation and inference for multivariate time series of counts using trawl processes

Modeling, simulation and inference for multivariate time series of counts using trawl processes Almut E. D. Veraart Imperial College London 2018 Workshop on Finance, Insurance, Probability and Statistics (FIPS 2018) Kings College London,

582 views • 34 slides

Table of contents Inference of high-dimensional VAR models Linear time series 1 Basics

Linear time series Structural analysis with VAR models Estimation of VAR models Numerical examples Table of contents Inference of high-dimensional VAR models Linear time series 1 Basics Univariate time series Seminar: Multivariate time

222 views • 5 slides

Continuous Imputation of Missing Values in Streams of Pattern-Determining Time Series Kevin

Continuous Imputation of Missing Values in Streams of Pattern-Determining Time Series Kevin Wellenzohn 1 ohlen 1 Michael H. B os 2 Johann Gamper 2 Hannes Mitterer 2 Anton Dign 1 Department of Computer Science University of Zurich 2 Faculty

356 views • 31 slides

Time Series Analysis and Mining with R Time Series Decomposi- tion Time Series Forecasting

R and Time Series Data Time Series Analysis and Mining with R Time Series Decomposi- tion Time Series Forecasting Yanchang Zhao Time Series Clustering Time Series RDataMining.com Classification http://www.rdatamining.com/ R Functions

1.91k views • 42 slides

Planning and Optimization D6. Pattern Databases: Pattern Selection Malte Helmert and Gabriele R

Planning and Optimization D6. Pattern Databases: Pattern Selection Malte Helmert and Gabriele R oger Universit at Basel November 8, 2017 Local Search Search Neighbourhood Literature Summary Content of this Course Tasks Progression/

586 views • 23 slides

Solving Percent Problems Word Problems Find a Pattern Estimation Problems Fraction Problems

Slide 1 / 142 Slide 2 / 142 Table of Contents Strategies & Plans for Problem Solving Rate Problems Solving Percent Problems Word Problems Find a Pattern Estimation Problems Fraction Problems Pre-Algebra LCM / GCF Problems Combination

477 views • 24 slides

Time for a Time-Change: A new Approach to Multivariate Intensity Models of Credit Risk Philipp J.

Time for a Time-Change: A new Approach to Multivariate Intensity Models of Credit Risk Philipp J. Sch onbucher D-MATH, ETH Z urich Princeton, May 2008 Outline Introduction 1 Pricing Single-Tranche CDOs Current Multivariate Intensity

681 views • 49 slides

Hydraulic Stimulation and Geophysical Fracture Monitoring in the -Project J. Orzol (1) , R. Jung

Hydraulic Stimulation and Geophysical Fracture Monitoring in the -Project J. Orzol (1) , R. Jung (1) , H. Buness (1) , T. Tischner (2) , R. Jatho (2) and P. Kehrer (2) (1): GGA-Institute, Hannover; (2): BGR, Hannover Objective supply

488 views • 18 slides

Geometric Algorithms Well-Separated Pair Decomposition & Spanners Motivation Connect a set

Geometric Algorithms Well-Separated Pair Decomposition & Spanners Motivation Connect a set of cities by a new street network. Motivation Connect a set of cities by a new street network. 1. idea: eucl. min. spanning tree Motivation

1.14k views • 67 slides

FOSS Assistant Professor Lyles School of Civil Engineering Geospatial Data Science

1 FOSS Assistant Professor Lyles School of Civil Engineering Geospatial Data Science Lab 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

327 views • 21 slides

Fast field survey with a smartphone A. Masiero F. Fissore, F. Pirotti, A. Guarnieri, A. Vettore

Introduction Positioning 3D reconstruction Conclusions Fast field survey with a smartphone A. Masiero F. Fissore, F. Pirotti, A. Guarnieri, A. Vettore CIRGEO Interdept. Research Center of Geomatics University of Padova Italy

428 views • 23 slides

Simulating Stencil-based Application on Future Xeon-Phi Processor PMBS workshop at SC15

Simulating Stencil-based Application on Future Xeon-Phi Processor PMBS workshop at SC15 Chitra Natarajan Carl Beckmann Anthony Nguyen Intel Corporation Intel Corporation Intel Corporation Mauricio Araya-Polo Tryggve Fossum Detlef Hohl

299 views • 19 slides

Status of Geophysics Interferometer KIW3@Academia Sinica, 1 Kouseki Miyo Mar 21, 2017 *

Status of Geophysics Interferometer KIW3@Academia Sinica, 1 Kouseki Miyo Mar 21, 2017 * Earthquake Research Institute ** Disaster Prevention Research Institute Institute for Cosmic Ray Research *A.Araya, *A.Takamori, **W.Morii, M.Ohashi

390 views • 17 slides

Aqua Program Status Claire L. Parkinson Aqua Project Scientist AIRS Science Team Meeting

Aqua Program Status Claire L. Parkinson Aqua Project Scientist AIRS Science Team Meeting Marriott Hotel, Greenbelt, Maryland September 26, 2006 Key Recent Aqua and Related Activities July 2006: Earth Science Reference Handbook

236 views • 23 slides

Utilization of Geostationary Satellite Observations for Air Quality Modeling During 2013

Utilization of Geostationary Satellite Observations for Air Quality Modeling During 2013 Discover-AQ Texas Campaign Arastoo Pour Biazar 1 , Andrew White 1 , Daniel Cohan 2 , Rui Zhang 2 , Maudood Khan 1 , Bright Dornblaser 3 , Richard McNider 1

533 views • 17 slides