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Understanding and predicting uncertain things are the central themes ofscientific evolution. Human beings revolve around these fears of uncertaintiesconcerning various aspects like a global pandemic, health, finances, to namebut a few. Dealing with this unavoidable part of life is far tougher due to thechaotic nature of these unpredictable activities. In the present article, weconsider a global network of identical chaotic maps, which splits into twodifferent clusters, despite the interaction between all nodes are uniform. Thestability analysis of the spatially homogeneous chaotic solutions provides acritical coupling strength, before which we anticipate such partialsynchronization. The distance between these two chaotic synchronizedpopulations often deviates more than eight times of standard deviation from itslong-term average. The probability density function of these highly deviatedvalues fits well with the Generalized Extreme Value distribution. Meanwhile,the distribution of recurrence time intervals between extreme events resemblesthe Weibull distribution. The existing literature helps us to characterize suchevents as extreme events using the significant height. These extremely highfluctuations are less frequent in terms of their occurrence. We determinenumerically a range of coupling strength for these extremely large butrecurrent events. On-off intermittency is the responsible mechanism underlyingthe formation of such extreme events. Besides understanding the generation ofsuch extreme events and their statistical signature, we furnish forecastingthese events using the powerful deep learning algorithms of an artificialrecurrent neural network. This Long Short-Term Memory (LSTM) can offer handyone-step forecasting of these chaotic intermittent bursts. We also ensure therobustness of this forecasting model with two hundred hidden cells in each LSTMlayer.

Extreme events in globally coupled chaotic maps