From time series to complex networks: The phase space coarse graining

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Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? (2018, background): “When setting the parameters of artiﬁcial neural networks, the learning rate, epochs, goals, and number of artiﬁcial neurons, all must be taken into account to achieve desirable results [25], [26].”
Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application (2017, background): “Although there have been abundant empirical results obtained using time series complex network algorithms [8-24], rigorous theoretical results are still lacking.”
Sensitivity and Nonlinearity of Thermoacoustic Oscillations (2018, methodology): “In thermoacoustics, Murugesan & Sujith (2015) used complex networks to investigate the scale invariance of combustion noise by applying a visibility algorithm (Lacasa et al. 2008) to time-series data from turbulent combustors.”
A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “Complex network theory has been recently applied to the analysis of time series and has yielded high-quality results [36 – 39].”