Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm

Nikolaou, Athanasia; Gutiérrez, Pedro Antonio; Durán, Antonio J.; Dicaire, Isabelle; Fernández-Navarro, Francisco; Hervás-Martı́nez, César

doi:10.1007/s00382-014-2405-0

Cited by 26 publications

(16 citation statements)

References 37 publications

(1 reference statement)

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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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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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“…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%

“…• Moreover, this automatic evaluation method has been used to perform a battery of experiments comparing the results obtained by a total of 10 different cluster validity indexes used as fitness functions. The new fitness functions improve the results of [16] to a great extent. These metrics, which measure cluster compression, are used to guide the algorithm to different solutions, which are later compared.…”

Section: Introductionmentioning

confidence: 95%

“…clustering, classification or motif detection) and for data compressing [12,13]. In this way, segmentation algorithms have been used in many different fields, such as paleoecological problems [14], phoneme recognition [15] or paleontological climate [16]. Some recent works have proposed the use of algebraic segmentation for the specific case of short-term [17,18] and short nonstationary time series [19].…”

Section: Introductionmentioning

confidence: 99%

“…A preliminary study in this direction was presented in [16], where an evolutionary segmentation algorithm was applied to analyse the segments preceding TPs, using the quotient of the number of segments divided by the sum of squared error of the clustering as the fitness function. This paper extends the previous work with the following contributions:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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