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Latent Time-Series Motifs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11430919_41openalex-snapshot
Locating Motifs in Time-Series Data
inferredcites →https://doi.org/10.1007/11871637_44openalex-snapshot
Discovering Patterns in Real-Valued Time Series
inferredcites →https://doi.org/10.1007/11893318_12openalex-snapshot
Mining Approximate Motifs in Time Series
inferredcites →https://doi.org/10.1007/978-3-319-05458-2_3openalex-snapshot
Exact Discovery of Length-Range Motifs
inferredcites →https://doi.org/10.1007/978-3-319-06538-0_8openalex-snapshot
Variable Length Motif-Based Time Series Classification
inferredcites →https://doi.org/10.1007/s00778-014-0370-1openalex-snapshot
ACME: A scalable parallel system for extracting frequent patterns from a very long sequence
inferredcites →https://doi.org/10.1007/s10115-014-0793-4openalex-snapshot
Enumeration of time series motifs of all lengths
inferredcites →https://doi.org/10.1007/s10710-011-9139-0openalex-snapshot
Sean Luke: essentials of metaheuristics
inferredcites →https://doi.org/10.1073/pnas.1211447110openalex-snapshot
A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion
inferredcites →https://doi.org/10.1108/ir.1999.04926fae.001openalex-snapshot
Lecture Notes in Computer Science 1205
inferredcites →https://doi.org/10.1109/icde.2008.4497550openalex-snapshot
FLAME: Shedding Light on Hidden Frequent Patterns in Sequence Datasets
inferredcites →https://doi.org/10.1109/icdm.2002.1183920openalex-snapshot
PERUSE: An unsupervised algorithm for finding recurring patterns in time series
inferredcites →https://doi.org/10.1109/icdm.2002.1183925openalex-snapshot
Mining motifs in massive time series databases
inferredcites →https://doi.org/10.1109/icdm.2007.52openalex-snapshot
Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery
inferredcites →https://doi.org/10.1109/icdm.2009.15openalex-snapshot
Finding Time Series Motifs in Disk-Resident Data
inferredcites →https://doi.org/10.1109/icdm.2013.111openalex-snapshot
Efficient Proper Length Time Series Motif Discovery
inferredcites →https://doi.org/10.1137/1.9781611972795.41openalex-snapshot
Exact Discovery of Time Series Motifs
inferredcites →https://doi.org/10.1137/1.9781611972801.73openalex-snapshot
Multiresolution Motif Discovery in Time Series
inferredcites →https://doi.org/10.1137/1.9781611972818.59openalex-snapshot
Time Series Motifs Statistical Significance
inferredcites →https://doi.org/10.1137/1.9781611972818.86openalex-snapshot
Online Discovery of Top-k Similar Motifs in Time Series Data
inferredcites →https://doi.org/10.1137/1.9781611972825.77openalex-snapshot
Visualizing Variable-Length Time Series Motifs
inferredcites →https://doi.org/10.1137/1.9781611977653openalex-snapshot
Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)
inferredcites →https://doi.org/10.1145/1281192.1281282openalex-snapshot
Detecting time series motifs under uniform scaling
inferredcites →https://doi.org/10.1145/1644873.1644875openalex-snapshot
Motif discovery in physiological datasets
inferredcites →https://doi.org/10.1145/1814245.1814255openalex-snapshot
Approximate variable-length time series motif discovery using grammar inference
inferredcites →https://doi.org/10.1145/1835804.1835941openalex-snapshot
Online discovery and maintenance of time series motifs
inferredcites →https://doi.org/10.1145/369133.369172openalex-snapshot
Finding motifs using random projections
inferredcites →https://doi.org/10.1145/956750.956808openalex-snapshot
Probabilistic discovery of time series motifs
inferredcites →https://doi.org/10.1161/01.cir.101.23.e215openalex-snapshot
PhysioBank, PhysioToolkit, and PhysioNet
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.5645openalex-snapshot
Discovering multi‐dimensional motifs from multi‐dimensional time series for air pollution control
inferred← citeshttps://doi.org/10.1007/978-3-031-24378-3_6openalex-snapshot
Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark
inferred← citeshttps://doi.org/10.1007/978-3-319-98809-2_16openalex-snapshot
Monitoring Range Motif on Streaming Time-Series
inferred← citeshttps://doi.org/10.1007/s00778-019-00573-wopenalex-snapshot
Coconut: sortable summarizations for scalable indexes over static and streaming data series
inferred← citeshttps://doi.org/10.1007/s00778-021-00655-8openalex-snapshot
Unsupervised and scalable subsequence anomaly detection in large data series
inferred← citeshttps://doi.org/10.1007/s00778-021-00677-2openalex-snapshot
Fast data series indexing for in-memory data
inferred← citeshttps://doi.org/10.1007/s10618-020-00685-wopenalex-snapshot
Matrix profile goes MAD: variable-length motif and discord discovery in data series
inferred← citeshttps://doi.org/10.1007/s10618-024-01032-zopenalex-snapshot
LoCoMotif: discovering time-warped motifs in time series
inferred← citeshttps://doi.org/10.1007/s10618-025-01169-5openalex-snapshot
Quantitative evaluation of motif sets in time series
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113287openalex-snapshot
Low correlation portfolio formation with preselection using rich relational data
inferred← citeshttps://doi.org/10.1109/codit.2019.8820629openalex-snapshot
MACD-Histogram-based Fully Convolutional Neural Networks for Classifying Time Series
inferred← citeshttps://doi.org/10.1109/icassp.2019.8682427openalex-snapshot
Discovering Optimal Variable-length Time Series Motifs in Large-scale Wearable Recordings of Human Bio-behavioral Signals
inferred← citeshttps://doi.org/10.1109/icedss.2017.8073683openalex-snapshot
A novel technique of motif discovery for medical big data using hadoop
inferred← citeshttps://doi.org/10.1109/pmbs56514.2022.00017openalex-snapshot
AppEKG: A Simple Unifying View of HPC Applications in Production
inferred← citeshttps://doi.org/10.1109/smc.2017.8122660openalex-snapshot
Multi-dimensional motif discovery in air pollution data
inferred← citeshttps://doi.org/10.1109/tkde.2024.3417303openalex-snapshot
Persistence-Based Motif Discovery in Time Series
inferred← citeshttps://doi.org/10.1145/3451397openalex-snapshot
Graph-Based Stock Recommendation by Time-Aware Relational Attention Network
inferred← citeshttps://doi.org/10.1145/3711896.3736551openalex-snapshot
Finding Repeated Structures in Time Series: Algorithms and Applications: A Unifying View of Time Series Motifs/Shapelets/Chains and Similar Primitives
inferred← citeshttps://doi.org/10.11591/ijece.v11i2.pp1208-1222openalex-snapshot
Estimation of posture and prediction of the elderly getting out of bed using a body pressure sensor
inferred← citeshttps://doi.org/10.48550/arxiv.1703.02144openalex-snapshot
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data
inferred← citeshttps://doi.org/10.48550/arxiv.2006.13713openalex-snapshot
Coconut: a scalable bottom-up approach for building data series indexes
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