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Clustering complex time‐series databases by using periodic components

https://doi.org/10.1002/sam.11341
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What this work says

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

inferredcites →https://doi.org/10.1007/978-1-4419-0320-4openalex-snapshot
Time Series: Theory and Methods
inferredcites →https://doi.org/10.1007/978-3-642-04277-5_32openalex-snapshot
Identifying Customer Profiles in Power Load Time Series Using Spectral Clustering
inferredcites →https://doi.org/10.1007/s10618-005-0039-xopenalex-snapshot
Characteristic-Based Clustering for Time Series Data
inferredcites →https://doi.org/10.1016/j.bspc.2008.07.003openalex-snapshot
Cluster analysis and classification of heart sounds
inferredcites →https://doi.org/10.1016/j.csda.2005.04.012openalex-snapshot
A periodogram-based metric for time series classification
inferredcites →https://doi.org/10.1016/j.csda.2007.06.001openalex-snapshot
Time series clustering and classification by the autoregressive metric
inferredcites →https://doi.org/10.1016/j.eswa.2010.05.050openalex-snapshot
Pattern recognition to forecast seismic time series
inferredcites →https://doi.org/10.1016/j.ijepes.2005.08.017openalex-snapshot
Electricity customer classification using frequency–domain load pattern data
inferredcites →https://doi.org/10.1016/j.neuroimage.2008.12.015openalex-snapshot
Cluster analysis of resting-state fMRI time series
inferredcites →https://doi.org/10.1016/j.patcog.2003.12.018openalex-snapshot
Time series clustering with ARMA mixtures
inferredcites →https://doi.org/10.1016/j.patcog.2005.01.025openalex-snapshot
Clustering of time series data—a survey
inferredcites →https://doi.org/10.1017/s0266466608080171openalex-snapshot
FORMALIZED DATA SNOOPING BASED ON GENERALIZED ERROR RATES
inferredcites →https://doi.org/10.1080/03610910701723971openalex-snapshot
Time Series Classification Based on Spectral Analysis
inferredcites →https://doi.org/10.1109/ijcnn.2015.7280329openalex-snapshot
Clustering analysis of the electrical load in european countries
inferredcites →https://doi.org/10.1109/isda.2011.6121776openalex-snapshot
Analysis of spectral clustering algorithms for linear and nonlinear time series
inferredcites →https://doi.org/10.1109/isgteurope.2014.7028919openalex-snapshot
Electrical load clustering: The Italian case
inferredcites →https://doi.org/10.1109/tpwrs.2005.852123openalex-snapshot
Short-Term Load Forecasting, Profile Identification, and Customer Segmentation: A Methodology Based on Periodic Time Series
inferredcites →https://doi.org/10.1111/1467-937x.00054openalex-snapshot
A Nonparametric Test for I(0)
inferredcites →https://doi.org/10.1111/1467-9892.00196openalex-snapshot
Estimation of the Dominating Frequency for Stationary and Nonstationary Fractional Autoregressive Models
inferredcites →https://doi.org/10.1111/1468-0262.00152openalex-snapshot
A Reality Check for Data Snooping
inferredcites →https://doi.org/10.1111/j.1467-9868.2009.00709.xopenalex-snapshot
Bootstrapping Frequency Domain Tests in Multivariate Time Series with an Application to Comparing Spectral Densities
inferredcites →https://doi.org/10.1111/j.1467-9892.1990.tb00048.xopenalex-snapshot
A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS
inferredcites →https://doi.org/10.1111/j.1467-9892.2011.00758.xopenalex-snapshot
Biological applications of time series frequency domain clustering
inferredcites →https://doi.org/10.1111/j.1468-0262.2005.00615.xopenalex-snapshot
Stepwise Multiple Testing as Formalized Data Snooping
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/10.1111/stan.12093openalex-snapshot
Spatial clustering of time series via mixture of autoregressions models and Markov random fields
inferredcites →https://doi.org/10.1137/1.9781611972757.40openalex-snapshot
On Periodicity Detection and Structural Periodic Similarity
inferredcites →https://doi.org/10.1145/1620432.1620472openalex-snapshot
Low-voltage electricity customer profiling based on load data clustering
inferredcites →https://doi.org/10.1145/2379776.2379788openalex-snapshot
Time-series data mining
inferredcites →https://doi.org/10.1198/016214504000000539openalex-snapshot
Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing
inferredcites →https://doi.org/10.1214/009053605000000084openalex-snapshot
Generalizations of the familywise error rate
inferredcites →https://doi.org/10.1214/11-aos900openalex-snapshot
On the range of validity of the autoregressive sieve bootstrap
inferredcites →https://doi.org/10.1214/aos/1013699998openalex-snapshot
The control of the false discovery rate in multiple testing under dependency
inferredcites →https://doi.org/10.2307/2288374openalex-snapshot
Spectral Analysis and Time Series.
inferredcites →https://doi.org/10.2307/4615733openalex-snapshot
A Simple Sequentially Rejective Multiple Test Procedure
inferredcites →https://doi.org/10.5120/8282-1278openalex-snapshot
Recent Techniques of Clustering of Time Series Data: A Survey

What points at this work

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

inferred← citeshttps://doi.org/10.1002/9781119502951.ch7openalex-snapshot
Repeated measurements
inferred← citeshttps://doi.org/10.1002/wics.1444openalex-snapshot
Time series clustering and classification via frequency domain methods
inferred← citeshttps://doi.org/10.1007/s00521-023-08313-6openalex-snapshot
Statistical arbitrage in the stock markets by the means of multiple time horizons clustering
inferred← citeshttps://doi.org/10.1016/j.eswa.2020.113840openalex-snapshot
Clustering of large scale QoS time series data in federated clouds using improved variable Chromosome Length Genetic Algorithm (CQGA)
inferred← citeshttps://doi.org/10.1080/2573234x.2019.1645574openalex-snapshot
Tree-Based Methods for Clustering Time Series Using Domain-Relevant Attributes
inferred← citeshttps://doi.org/10.1109/ecmr65884.2025.11163256openalex-snapshot
Visual Perception of Repetitive Human Motions: Temporal Localization, Grouping and Repetitions Counting
inferred← citeshttps://doi.org/10.1109/eeeic/icpseurope51590.2021.9584633openalex-snapshot
Load Profiles Clustering and Knowledge Extraction to Assess Actual Usage of Telecommunication Sites
inferred← citeshttps://doi.org/10.1109/tia.2023.3240669openalex-snapshot
A Machine Learning Based Methodology for Load Profiles Clustering and Non-Residential Buildings Benchmarking
inferred← citeshttps://doi.org/10.3390/sym14061237openalex-snapshot
Using K-Means Clustering in Python with Periodic Boundary Conditions
inferred← citeshttps://doi.org/10.4018/978-1-7998-9220-5.ch182openalex-snapshot
Statistical Model Selection for Seasonal Big Time Series Data
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