2019
DOI: 10.1063/1.5112782
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A sliding window-based algorithm for faster transformation of time series into complex networks

Abstract: A new alternative method to approximate the Visibility Graph (VG) of a time series has been introduced here. It exploits the fact that most of the nodes in the resulting network are not connected to those that are far away from them. This means that the adjacency matrix is almost empty, and its nonzero values are close to the main diagonal. This new method is called Sliding Visibility Graph (SVG). Numerical tests have been performed for several time series, showing a time efficiency that scales linearly with t… Show more

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Cited by 10 publications
(7 citation statements)
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“…Sliding-window is widely applied in various areas and related algorithms are proved to be of high computational efficiency and able to reduce the required storage [22]. Hence, an improved method is developed as in [16] by introducing the sliding-window idea into the network constructing process of VG to improve construction efficiency. Because of sliding-window, the aforementioned criterion is only necessary to be applied between a data point and a certain point within the sliding-window.…”
Section: Model Descriptionmentioning
confidence: 99%
See 3 more Smart Citations
“…Sliding-window is widely applied in various areas and related algorithms are proved to be of high computational efficiency and able to reduce the required storage [22]. Hence, an improved method is developed as in [16] by introducing the sliding-window idea into the network constructing process of VG to improve construction efficiency. Because of sliding-window, the aforementioned criterion is only necessary to be applied between a data point and a certain point within the sliding-window.…”
Section: Model Descriptionmentioning
confidence: 99%
“…Thus, the necessary times of applying the above discriminate criteria will be reduced tremendously. As in [16], we suppose the time series data is composed of N data points while the selected sliding-window length equals to W. Then, the network construction procedure through SVG is provided as: Examples are provided in Figure 1 which illustrates the construction process through VG and SVG with a window length of 4. For Figures 1B-D, the data points indicated by red columns are within the sliding-window, whereas those represented by blue columns are outside the sliding-window.…”
Section: Model Descriptionmentioning
confidence: 99%
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“…This method is then extended to the application of non-stationary binary channel noise estimation. Due to the high complexity of the particle filtering algorithm, the literature [23,24] proposes a nonstationary source local parameter estimation algorithm based on expected propagation. At present, the technology development of the blood oxygen saturation monitor is relatively mature, and its overall trend is to miniaturize, that is, the collection, processing and display are integrated [25,26], but the current blood oxygen saturation monitor still has many disadvantages, such as high cost, Large power consumption, poor real-time performance, low measurement accuracy, poor stability, and need to be further intelligent and networked [27].…”
Section: Introductionmentioning
confidence: 99%