Results 81 to 90 of about 303,598 (207)

Unraveling Meteorological Dynamics: A Two-Level Clustering Algorithm for Time Series Pattern Recognition with Missing Data Handling

Stats
Identifying regions with similar meteorological features is of both socioeconomic and ecological importance. Towards that direction, useful information can be drawn from meteorological stations, and spread in a broader area.
Ekaterini Skamnia, Eleni S. Bekri, Polychronis Economou   +2 more
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
doaj   +1 more source

Time-series clustering for sensor fault detection in large-scale Cyber-Physical Systems [PDF]

Large-scale Cyber-Physical Systems (CPSs) are information systems that involve a vast network of sensor nodes and other devices that stream observations in real-time and typically are deployed in uncontrolled, broad geographical terrains.
Brimicombe, Allan, Falcarin, Paolo, Alwan, A., Ghorashi, Seyed, Baravalle, Andrea, Ciupala, Anca   +5 more
core   +1 more source

Exploring Optimal Strategies for Small Hydro Power Forecasting: Training Periods and Methodological Variations

Engineering Proceedings
This study investigates optimal training intervals for small hydro power regression models, crucial for accurate forecasts in diverse conditions, particularly focusing on Portugal’s small hydro portfolio.
Duarte Lopes, Isabel Preto, David Freire
doaj   +1 more source

Improved robustness in time series analysis of gene expression data by polynomial model based clustering

, 2006
Microarray experiments produce large data sets that often contain noise and considerable missing data. Typical clustering methods such as hierarchical clustering or partitional algorithms can often be adversely affected by such data.
Liu, X, Michael Hirsch, Martin, N, Kellam, P., Xiaohui Liu, Orengo, C., Paul Kellam, Liu, X., Swift, S, Swift, S., Kellam, P, Hirsch, M, Stephen Swift, Tucker, A., Hirsch, M., Tucker, A, Orengo, C, Martin, Nigel, Allan Tucker, Nigel Martin, Christine Orengo   +20 more
core   +1 more source

Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering [PDF]

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative data set. Further,
Sylvia Frühwirth-Schnatter, Christoph Pamminger, Rudolf Winter-Ebmer, Andrea Weber   +3 more
core   +2 more sources

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
core   +1 more source

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
core  

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
core  

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