The problem of clustering of time-series data is formally defined as follows: Definition 1: Time-series clustering, given a dataset of n time-series data D={F1,F2,..,Fn}, the process of unsupervised partitioning
Time-series clustering – A decade review. Information Systems 53, 16–38, doi: 10.1016/j.is.2015.04.007 (2015). 30. Rodrigues, F. M. & Diniz-Filho, J. A. F. Hierarchical structure of genetic distances: effects of matrix size, spatial distribution and correlation structure among gene ...
In this study, we present a forecasting procedure for time-series supported by unsupervised learning of consistent clusters, specifically designed to make one-day-ahead forecasts of sow feed intake during lactation. Using data from different farms, our approach first uses time-series clustering to au...
This study proposes a novel methodology for aquifer delineation using time-series clustering of groundwater-level data. The modular clustering framework utilizes hierarchical agglomerative clustering and a custom hydrology-specific distance function. This accounts for the variability in the length, temporal ...
Time series data analysis, especially forecasting, classification, imputation, and anomaly detection, has gained a lot of research attention in recent year
Y. (2015), ‘Time-series clustering–A decade review’, Information Systems 53, 16–38. (Open in a new window)Google Scholar Aue, A., Horváth, L. and F. Pellatt, D. (2017), ‘Functional generalized autoregressive conditional heteroskedasticity’, Journal of Time Series Analysis 38(1)...
Financial time series clustering finds application in forecasting, noise reduction and enhanced index tracking. The central theme in all the available clustering algorithms is the dissimilarity measure employed by the algorithm. The dissimilarity measure
TICC A python solver for efficiently segmenting and clustering a multivariate time series. tick Module for statistical learning, with a particular emphasis on time-dependent modelling. timemachines Continuously evaluated, functional, incremental, time-series forecasting. TimeSeers A hierarchical Bayesian Time...
As technology advances, a large number of time series data have emerged in all walks of life. Clustering is a key technique for analysing time series data. However, most of the existing clustering methods calculate the distance of a single discrete data point, but cannot be applied to continuo...
Based on the time series representation, different mining tasks can be found in the literature and they can be roughly classified into four fields: pattern discovery and clustering, classification, rule discovery and summarization. Some of the research concentrates on one of these fields, while the...