Algebraic segmentation of short nonstationary time series based on evolutionary prediction algorithms

Palivonaitė, Rita; Lukoševičiūtė, Kristina; Ragulskis, Minvydas

doi:10.1016/j.neucom.2013.05.013

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…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%

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Among the various forecasting models [11,12,13], due to its superiority in limited data forecasting, grey model has potential to become a very powerful tool for natural gas forecasting [14,15], in particular, the grey model of the first-order and one variable (GM(1,1)) [16]. The GM(1,1) model achieved significant improvements in prediction and simulation accuracy from the following aspects; a generating operator [17], parameter estimation [18,19], differential equations [20], non-equigap grey model [21], hybrid models [22,23,24], etc.…”

Section: Introductionmentioning

confidence: 99%

Forecasting natural gas production and consumption using grey model with latent information function: The cases of China and USA

Zhang

Zhao

2019

Scientia Iranica

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The aim of the paper is to develop a grey model for short term forecasting of natural gas consumption and production in China and USA respectively. To enhance its prediction accuracy, the outliers are found by the error of the latent information function, and then corrected according to the test sample and the future trend. The sequence with corrected outliers is used to construct a grey model. The proposed model is employed to predict the natural gas consumption and production in China and USA. The results have demonstrated that the proposed model can raise the forecast accuracy of the grey model, and it also indicates that China will inevitably face a massive expansion in natural gas imports.

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Algebraic Level-Set Approach for the Segmentation of Financial Time Series

Palivonaitė

Lukoševičiūtė

Ragulskis

2014

Applications of Evolutionary Computation

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Algebraic segmentation of short nonstationary time series based on evolutionary prediction algorithms

Cited by 11 publications

References 43 publications

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

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

Forecasting natural gas production and consumption using grey model with latent information function: The cases of China and USA

Algebraic Level-Set Approach for the Segmentation of Financial Time Series

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