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Flavio Maria Emanuele Pons

Researcher at University of Bologna

Publications -  31
Citations -  334

Flavio Maria Emanuele Pons is an academic researcher from University of Bologna. The author has contributed to research in topics: Climatology & Computer science. The author has an hindex of 9, co-authored 22 publications receiving 211 citations. Previous affiliations of Flavio Maria Emanuele Pons include Université Paris-Saclay.

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Advance booking and hotel price variability online: Any opportunity for business customers?

TL;DR: In this paper, the authors consider daily online best available rates for 357 hotels in Milan and Rome, up to an advance booking of 29 days, and analyse price trajectories, finding that dynamic pricing strategies with no established trend towards the arrival date are prevalent, with a predominance of decreasing trajectories for lower scale hotels during fairs.
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Dynamical Systems Theory Sheds New Light on Compound Climate Extremes in Europe and Eastern North America

TL;DR: In this paper, the co-recurrence ratio (α) is used to elucidate the dependence structure of compound extremes, which is grounded in dynamical systems theory and has been used to study compound extremes.
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Early warnings indicators of financial crises via auto regressive moving average models

TL;DR: It is observed that ϒ computed for the Dow Jones, Standard and Poor’s and EURO STOXX 50 indexes provides an effective early warning indicator which allows for detection of the crisis events that showed precursors.
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The persistent signature of tropical cyclones in ambient seismic noise

TL;DR: In this article, the spectral amplitude of ambient seismic noise, and notably the short-period secondary microseisms, has a strong relationship with tropical cyclone intensity and can be employed to extract information on the tropical cyclones.
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Competition patterns, spatial and advance booking effects in the accommodation market online

TL;DR: In this paper, the authors study the characteristics of dynamic competition online in the accommodation market by exploiting daily best available rates (BAR) provided by online travel agencies, up to 28 days of advance booking.