Clustering of time series data - a survey

By T. Warren Liao. 2005

2,649 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability, Signal Processing

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Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, methodology): “Following Warren Liao (2005), we can distinguish three main approaches to time series clustering, namely algorithms that work directly with distances on raw data points (distancebased), indirectly with features extracted from the raw data (feature-based), or indirectly with models built from the…”
Unsupervised Semantic Parsing of Video Collections (2015, methodology): “In order to adopt these metrics into unsupervised setting, we use cluster similarity measure(csm)[37] which enables us to”
Time-series clustering - A decade review (2015, methodology): “Cluster Similarity Measure ( CSM ) : CSM [ 16 ] is a simple metric used for validity of clusters in time - series domain [ 18 , 26 , 107 , 223 ] .”
Inverse analysis in non-parametric multivariate analyses: distinguishing groups of associated species which covary coherently across samples (2013, methodology): “, 2002) and many others (Liao, 2005).”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, methodology): “All approaches generally modify existing algorithms, either by replacing the default distance measures with a version that is more suitable for comparing time series (rawbased methods), or by transforming the sequences into “flat” data so that they can be directly used in classic algorithms (feature”
The [Limited] Impact of Weather on Tourist Behavior in an Urban Destination (2015, methodology): “In order to deal with the elements of time and sequence, three approaches are available (Liao 2005): a raw-data-based approach requiring substitution of standard similarity measures with ones that take time into account; the feature-based approach, in which raw data are converted into a feature…”
Unsupervised Learning and Segmentation of Complex Activities from Video (2018, methodology): “To make a valid comparison, since our work is aimed at finding entire segments, we randomly select a frame from each segment and then find a one-to-one mapping based on [14].”
kml and kml3d: R Packages to Cluster Longitudinal Data (2015, background): “…initial conditions, center calculation methods, distances between individuals, distances between clusters, or stopping criteria, while others sequentialize the cluster allocation or randomly disrupt some individuals to escape from local minima (see Liao 2005 for a survey of all these techniques).”
Trajectory-Based Anomalous Event Detection (2008, background): “For a survey on trajectory (and more generally time series) clustering, see [16].”
Highly Comparative Feature-Based Time-Series Classification (2014, background): “…of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.”
External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews? (2014, methodology): “See the list of papers we found by our query for event-bias detection [12,41,62–77].”
Data-driven Automatic Treatment Regimen Development and Recommendation (2016, methodology): “For continuous time series data, a lot of representation methods and similarity measurement methods have been developed [1, 2, 10, 22].”
TSclust: An R Package for Time Series Clustering (2014, background): “An excellent survey on time series clustering can be seen in Liao (2005) and references therein, although signiﬁcant new contributions have been made subsequently.”
Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns (2013, background): “A survey of distance metrics for time series clustering can be found in [17].”