Results 61 to 70 of about 18,151,622 (163)

ESTIMATE OF TIME SERIES SIMILARITY BASED ON MODELS

Міжнародний науково-технічний журнал "Проблеми керування та інформатики"
Determining the measure of the distance between time series is the starting point for many data mining tasks such as clustering and classification. Clustering is the main method of teaching without a teacher, which is used to divide data into groups ...
Т.В. Кнігніцька
doaj   +1 more source

Similarity dynamical clustering algorithm based on multidimensional shape features for time series

工程科学学报, 2017
Traditional data mining methods are difficult to deal with the high dimensionality and dynamics characteristic of the time series. Therefore, in this study, a similarity dynamical clustering algorithm based on multidimensional shape features for time ...
WANG Ling, MENG Jian-yao, XU Pei-pei, PENG Kai-xiang   +3 more
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Subsequence Time Series Clustering

, 2009
Clustering analysis is a tool used widely in the Data Mining community and beyond (Everitt et al. 2001). In essence, the method allows us to “summarise” the information in a large data set X by creating a very much smaller set C of representative points (
Jason Chen
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Multiple Time Series Forecasting with Temporal Fusion Transformers [PDF]

, 2023
openThe goal of this thesis is to present the Temporal Fusion Transformer model and to evaluate its forecasting capabilities across multiple time series.
ZIRALDO, GAIA
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Time series similarity measurement based on fractionaldifferential and its application

四川大学学报. 自然科学版, 2023
Similarity measures of time series are the basis for time series clustering, classification and other related time series analysis. The traditional distance-based similarity measure ignores the possible temporal connections of time series and treats time
YAN Wen-Peng, WANG Zhi-Tao, YUAN Xiao
doaj  

Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models [PDF]

Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov chains are discussed. For Markov chain clustering the in- dividual transition probabilities are fixed to a group-specific transition matrix.
Sylvia Frühwirth-Schnatter, Christoph Pamminger   +1 more
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Performance evaluation of a residual-based approach and clustering for time series with application to demand forecasting

, 2022
reservedDemand forecasting is a critical issue in predicting customer demand and improving the corresponding management plans; this is commonly done through the analysis of the so-called time series. However, it is possible that adequacy of a forecasting
SINIGAGLIA, ANDREA
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Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering

Tsinghua Science and Technology
Time series clustering is a challenging problem due to the large-volume, high-dimensional, and warping characteristics of time series data. Traditional clustering methods often use a single criterion or distance measure, which may not capture all the ...
Rui Wang, Wenhua Li, Kaili Shen, Tao Zhang, Xiangke Liao   +4 more
doaj   +1 more source

An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns

ISPRS International Journal of Geo-Information, 2016
Current time series clustering algorithms fail to effectively mine clustering distribution characteristics of time series data without sufficient prior knowledge.
Xiaomi Wang, Yaolin Liu, Yiyun Chen, Yi Liu   +3 more
doaj   +1 more source

Fuzzy Clustering Models for Gene Expression Data Analysis [PDF]

With the advent of microarray technology, it is possible to monitor gene expression of tens of thousands of genes in parallel. In order to gain useful biological knowledge, it is necessary to study the data and identify the underlying patterns, which ...
Wang, Yu
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