Cluster-based genetic segmentation of time series with DWT

Tseng, Vincent S.; Chen, Chun‐Hao; Huang, Pai-Chieh; Hong, Tzung-Pei

doi:10.1016/j.patrec.2009.05.013

Cited by 34 publications

(7 citation statements)

References 30 publications

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“…However, their extension/adaptation to SITS in order to exploit both the spatial and temporal information contained in these data remains an open-issue. Indeed, although several methods have been proposed in order to map segments from one image to another (Gueguen et al, 2006;Bovolo, 2009), to directly build spatio-temporal segments (Fan et al, 1996;Moscheni et al, 1998;Tseng et al, 2009), or even to consider object-based features (Hall & Hay, 2003;Niemeyer et al, 2008;Hofmann et al, 2008;Schopfer et al, 2008;Tiede et al, 2011), their scalability to wide sensed areas and their robustness to local disturbance (temporally and spatially) remain problematic. The use of 3D-dedicated methods indeed requires a high temporal continuity; this constraint is however rarely fulfilled by SITS, where the average time-delay between two images is usually too high.…”

Section: State Of the Artmentioning

confidence: 99%

Spatio-temporal reasoning for the classification of satellite image time series

Petitjean

Kurtz

Passat

et al. 2012

Pattern Recognition Letters

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Satellite Image Time Series (SITS) analysis is an important domain with various applications in land study. In the coming years, both high temporal and high spatial resolution SITS will become available. In the classical methodologies, SITS are studied by analyzing the radiometric evolution of the pixels with time. When dealing with high spatial resolution images, object-based approaches are generally used in order to exploit the spatial relationships of the data. However, these approaches require a segmentation step to provide contextual information about the pixels. Even if the segmentation of single images is widely studied, its generalization to series of images remains an open-issue. This article aims at providing both temporal and spatial analysis of SITS. We propose first segmenting each image of the series, and then using these segmentations in order to characterize each pixel of the data with a spatial dimension (i.e., with contextual information). Providing spatially characterized pixels, pixel-based temporal analysis can be performed. Experiments carried out with this methodology show the relevance of this approach and the significance of the resulting extracted patterns in the context of the analysis of SITS.

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Section: State Of the Artmentioning

confidence: 99%

Spatio-temporal reasoning for the classification of satellite image time series

Petitjean

Kurtz

Passat

et al. 2012

Pattern Recognition Letters

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“…• Discovering similar patterns: The main objective is the discovery and characterization of important events in the time series, by obtaining similar segments. The methods of Chung et al [20], Tseng et al [21] and Nikolaou et al [10] are all based on evolutionary algorithms, given the large size of the search space when deciding the cut points. • Approximating the time series by a set of simple models, e.g.…”

Section: Time Series Segmentationmentioning

confidence: 99%

Time-Series Clustering Based on the Characterization of Segment Typologies

Guijo-Rubio

Durán-Rosal

Gutiérrez

et al. 2021

IEEE Trans. Cybern.

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Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time series objects of the dataset. In this paper, we propose a novel technique of time series clustering based on two clustering stages. In a first step, a least squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all the segments are projected into same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmentation. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against two state-of-the-art methods, showing that the performance of this methodology is very promising.

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“…, m − 1, are the parameters to be determined by the algorithm. As done in [23], we extend this setting by trying to group the segments into k different classes or clusters (k < m), where k is a parameter defined by the user. In this way, each segment, s l , will be associated to a class label:…”

Section: Segmentation Algorithmmentioning

confidence: 99%

“…The Genetic Algorithm considered in this paper can be included in the area of time series segmentation [7,24,25,26,9,10,11,17,13]. Our main objective is to devise an unsupervised methodology to identify time segments with similar statistical behaviour [23]. This is a necessary step to study the characteristics of these time segments and be able to analyse the temporal transitions between different states (i.e., types of segments) or to construct a prediction model with a dynamic-window [16].…”

Section: Summary Of the Algorithmmentioning

confidence: 99%

On the use of evolutionary time series analysis for segmenting paleoclimate data

et al. 2019

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Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series.

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Cluster-based genetic segmentation of time series with DWT

Cited by 34 publications

References 30 publications

Spatio-temporal reasoning for the classification of satellite image time series

Spatio-temporal reasoning for the classification of satellite image time series

Time-Series Clustering Based on the Characterization of Segment Typologies

On the use of evolutionary time series analysis for segmenting paleoclimate data

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