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Significant motifs in time series

https://doi.org/10.1002/sam.11134
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/0-387-25465-x_25openalex-snapshot
Statistical Methods for Data Mining
inferredcites →https://doi.org/10.1007/11893318_12openalex-snapshot
Mining Approximate Motifs in Time Series
inferredcites →https://doi.org/10.1007/978-3-642-24477-3_12openalex-snapshot
Multiple Hypothesis Testing in Pattern Discovery
inferredcites →https://doi.org/10.1007/s10618-007-0070-1openalex-snapshot
Efficient mining of understandable patterns from multivariate interval time series
inferredcites →https://doi.org/10.1007/s10994-005-5829-2openalex-snapshot
Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle
inferredcites →https://doi.org/10.1007/s10994-007-5006-xopenalex-snapshot
Discovering Significant Patterns
inferredcites →https://doi.org/10.1007/s10994-008-5045-yopenalex-snapshot
Discovering significant patterns
inferredcites →https://doi.org/10.1007/s10994-008-5098-yopenalex-snapshot
Mining probabilistic automata: a statistical view of sequential pattern mining
inferredcites →https://doi.org/10.1016/j.csda.2008.11.028openalex-snapshot
How accurate are the extremely small -values used in genomic research: An evaluation of numerical libraries
inferredcites →https://doi.org/10.1016/j.elstat.2004.02.007openalex-snapshot
Fractal simulation of soil breakdown under lightning current
inferredcites →https://doi.org/10.1023/a:1024988512476openalex-snapshot
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
inferredcites →https://doi.org/10.1073/pnas.1530509100openalex-snapshot
Statistical significance for genomewide studies
inferredcites →https://doi.org/10.1089/10665270050081469openalex-snapshot
An Overview on the Distribution of Word Counts in Markov Chains
inferredcites →https://doi.org/10.1089/106652701752236179openalex-snapshot
Numerical Comparison of Several Approximations of the Word Count Distribution in Random Sequences
inferredcites →https://doi.org/10.1089/cmb.2007.0137openalex-snapshot
Assessing the Exceptionality of Network Motifs
inferredcites →https://doi.org/10.1093/bioinformatics/btl511openalex-snapshot
DASS: efficient discovery andp-value calculation of substructures in unordered data
inferredcites →https://doi.org/10.1093/bioinformatics/btl662openalex-snapshot
Computing exactP-values for DNA motifs
inferredcites →https://doi.org/10.1093/bioinformatics/btn525openalex-snapshot
Faster exact Markovian probability functions for motif occurrences: a DFA-only approach
inferredcites →https://doi.org/10.1093/bioinformatics/btp188openalex-snapshot
Efficient exact motif discovery
inferredcites →https://doi.org/10.1109/cidm.2007.368869openalex-snapshot
Evaluating Protein Motif Significance Measures: A Case Study on Prosite Patterns
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.2005.79openalex-snapshot
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
inferredcites →https://doi.org/10.1109/icdm.2006.79openalex-snapshot
GraphRank: Statistical Modeling and Mining of Significant Subgraphs in the Feature Space
inferredcites →https://doi.org/10.1109/icdm.2009.15openalex-snapshot
Finding Time Series Motifs in Disk-Resident Data
inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02031.xopenalex-snapshot
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
inferredcites →https://doi.org/10.1126/science.298.5594.824openalex-snapshot
Network Motifs: Simple Building Blocks of Complex Networks
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/10.1142/s0219720006002028openalex-snapshot
COMPARISON OF STATISTICAL SIGNIFICANCE CRITERIA
inferredcites →https://doi.org/10.1145/1014052.1014094openalex-snapshot
On the discovery of significant statistical quantitative rules
inferredcites →https://doi.org/10.1145/1281192.1281282openalex-snapshot
Detecting time series motifs under uniform scaling
inferredcites →https://doi.org/10.1145/1401890.1401966openalex-snapshot
i SAX
inferredcites →https://doi.org/10.1145/1835804.1835941openalex-snapshot
Online discovery and maintenance of time series motifs
inferredcites →https://doi.org/10.1145/775047.775128openalex-snapshot
Finding surprising patterns in a time series database in linear time and space
inferredcites →https://doi.org/10.1145/956750.956808openalex-snapshot
Probabilistic discovery of time series motifs
inferredcites →https://doi.org/10.1186/1471-2105-8-84openalex-snapshot
Statistical tests to compare motif count exceptionalities
inferredcites →https://doi.org/10.1186/1748-7188-1-5openalex-snapshot
Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics
inferredcites →https://doi.org/10.1186/1748-7188-2-13openalex-snapshot
Exact p-value calculation for heterotypic clusters of regulatory motifs and its application in computational annotation of cis-regulatory modules
inferredcites →https://doi.org/10.14778/1454159.1454226openalex-snapshot
Querying and mining of time series data
inferredcites →https://doi.org/10.2307/4615733openalex-snapshot
A Simple Sequentially Rejective Multiple Test Procedure
inferredcites →https://doi.org/10.57805/revstat.v4i1.25openalex-snapshot
Network Motifs: Mean and Variance for the Count

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/sam.11191openalex-snapshot
Flow field forecasting for univariate time series
inferred← citeshttps://doi.org/10.1002/widm.1122openalex-snapshot
Temporal interval pattern languages to characterize time flow
inferred← citeshttps://doi.org/10.1007/978-3-032-15535-1_2openalex-snapshot
The Next Motif: Tapping into Recurrence Dynamics and Precursor Signals to Forecast Events of Interest
inferred← citeshttps://doi.org/10.1007/978-3-319-06538-0_8openalex-snapshot
Variable Length Motif-Based Time Series Classification
inferred← citeshttps://doi.org/10.1007/978-3-319-22635-4_18openalex-snapshot
Fast Frequent Episode Mining Based on Finite-State Machines
inferred← citeshttps://doi.org/10.1007/978-3-319-70990-1_75openalex-snapshot
The Motif Detection of Turning Points in Stock Time Series
inferred← citeshttps://doi.org/10.1007/978-3-642-45269-7_4openalex-snapshot
Efficient Mining of Lag Patterns in Evolving Time Series
inferred← citeshttps://doi.org/10.1007/s10618-016-0467-9openalex-snapshot
Skopus: Mining top-k sequential patterns under leverage
inferred← citeshttps://doi.org/10.1007/s11227-013-1010-zopenalex-snapshot
Effective connectivity analysis of fMRI data based on network motifs
inferred← citeshttps://doi.org/10.1016/j.patcog.2025.111900openalex-snapshot
Cutting through the noise: Explaining residuals in multivariate time series with motif analysis
inferred← citeshttps://doi.org/10.1016/j.patrec.2026.04.020openalex-snapshot
On why and how statistical significance criteria can guide multivariate time series motif analysis
inferred← citeshttps://doi.org/10.1051/itmconf/20268904002openalex-snapshot
Uncovering Periodic Templates in Algorithm-Driven Virtual Economies: A Hybrid Deep Learning Approach with Application to Arknight
inferred← citeshttps://doi.org/10.1080/13658816.2024.2320149openalex-snapshot
Geo-SigSPM: mining geographically interesting and significant sequential patterns from trajectories
inferred← citeshttps://doi.org/10.1109/cbmi.2013.6576567openalex-snapshot
Fast repetition detection in TV streams using duration patterns
inferred← citeshttps://doi.org/10.1109/swc50871.2021.00095openalex-snapshot
Discovering COVID-19 Induced Shifts in Refined Petroleum Products Demand: A Sequence-based Time Series Mining Approach
inferred← citeshttps://doi.org/10.1109/tec.2023.3294540openalex-snapshot
A Unified Deep Learning Optimization Paradigm for Lithium-Ion Battery State-of-Health Estimation
inferred← citeshttps://doi.org/10.1109/tvcg.2018.2851227openalex-snapshot
COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series
inferred← citeshttps://doi.org/10.1145/2940329openalex-snapshot
Latent Time-Series Motifs
inferred← citeshttps://doi.org/10.1186/s12864-023-09214-7openalex-snapshot
High-throughput time series expression profiling of Plasmopara halstedii infecting Helianthus annuus reveals conserved sequence motifs upstream of co-expressed genes
inferred← citeshttps://doi.org/10.14778/2735471.2735476openalex-snapshot
Rare time series motif discovery from unbounded streams
inferred← citeshttps://doi.org/10.3233/ida-194759openalex-snapshot
Spatial-time motifs discovery
inferred← citeshttps://doi.org/10.3390/engproc2021005045openalex-snapshot
Financial Time Series: Market Analysis Techniques Based on Matrix Profiles †
inferred← citeshttps://doi.org/10.5539/mer.v2n1p71openalex-snapshot
Identification Method for Evolution of Time Series with Poor Information Using Grey System Theory
inferred← citeshttps://doi.org/10.70675/ace06e67z081ez4509z9382zbf963394f9bbopenalex-snapshot
Time series representation for classification : a motif-based approach
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