Identification of Colombian coffee price dynamics.

The daily Colombian coffee price is a chaotic signal that has emerged from a complex economic system. This work proposes to identify its dynamics by means of two models: a single multiscroll Chua system and the coupling of two of these systems. Models are fine-tuned through an artificial bee colony optimization algorithm. Results show that this approach can reconstruct the price signal in terms of several statistics and points out a way for its long-term forecasting.

Hidden complexity in Life-like rules.

An alternative way to study the rules of life-like cellular automata is presented. The proposed perspective studies some multifractal and informational properties of Boolean functions behind these rules. Results from this approach challenge the traditional argument about the simplicity of Lifelike rules.

Information Dynamics in Urban Crime.

Information production in both space and time has been highlighted as one of the elements that shapes the footprint of complexity in natural and socio-technical systems. However, information production in urban crime has barely been studied. This work copes with this problem by using multifractal analysis to characterize the spatial information scaling in urban crime reports and nonlinear processing tools to study the temporal behavior of this scaling. Our results suggest that information scaling in urban crime exhibits dynamics that evolve in low-dimensional chaotic attractors, and this can be observed in several spatio-temporal scales, although some of them are more favorable than others. This evidence has practical implications in terms of defining the characteristic scales to approach urban crime from available data and supporting theoretical perspectives about the complexity of urban crime.

Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis.

OBJECTIVE: This article presents a model of a dengue and severe dengue epidemic in Colombia based on the cases reported between 1995 and 2011. METHODOLOGY: We present a methodological approach that combines multiresolution analysis and fuzzy systems to represent cases of dengue and severe dengue in Colombia. The performance of this proposal was compared with that obtained by applying traditional fuzzy modeling techniques on the same data set. This comparison was obtained by two performance measures that evaluate the similarity between the original data and the approximate signal: the mean square error and the variance accounted for. Finally, the predictive ability of the proposed technique was evaluated to forecast the number of dengue and severe dengue cases in a horizon of three years (2012-2015). These estimates were validated with a data set that was not included into the training stage of the model. RESULTS: The proposed technique allowed the creation of a model that adequately represented the dynamic of a dengue and severe dengue epidemic in Colombia. This technique achieves a significantly superior performance to that obtained with traditional fuzzy modeling techniques: the similarity between the original data and the approximate signal increases from 21.13% to 90.06% and from 18.90% to 76.83% in the case of dengue and severe dengue, respectively. Finally, the developed models generate plausible predictions that resemble validation data. The difference between the cumulative cases reported from January 2012 until July 2013 and those predicted by the model for the same period was 24.99% for dengue and only 4.22% for severe dengue. CONCLUSIONS: The fuzzy model identification technique based on multiresolution analysis produced a proper representation of dengue and severe dengue cases for Colombia despite the complexity and uncertainty that characterize this biological system. Additionally, the obtained models generate plausible predictions that can be used by surveillance authorities to support decision-making oriented to designing and developing control strategies.