Stochastic dynamic models of curve crossing phenomena in condensed phases

1987 ◽  
Vol 86 (7) ◽  
pp. 3836-3844 ◽  
Author(s):  
Raymond E. Cline ◽  
Peter G. Wolynes
Keyword(s):  
Dynamic Models ◽  
Stochastic Dynamic ◽  
Condensed Phases ◽  
Curve Crossing

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional

scholarly journals Stochastic dynamic models and Chebyshev splines

2014 ◽  
Vol 42 (4) ◽  
pp. 610-634 ◽  
Author(s):  
Ruzong Fan ◽  
Bin Zhu ◽  
Yuedong Wang
Keyword(s):  
Dynamic Models ◽  
Stochastic Dynamic ◽  
Chebyshev Splines

Dynamic pricing for online hotel demand: The case of resort hotels in Majorca

2019 ◽  
Vol 26 (2) ◽  
pp. 268-283 ◽  
Author(s):  
Aldric Vives ◽  
Marta Jacob
Keyword(s):  
Dynamic Pricing ◽  
Dynamic Models ◽  
Customer Behavior ◽  
Optimal Pricing ◽  
Stochastic Dynamic ◽  
Elastic Demand ◽  
Demand Functions ◽  
Elasticity Of Demand ◽  
Price Optimization ◽  
Resort Hotels

Online customer behavior in terms of price elasticity of demand and the effect of time along the booking horizon are key requirements for the price optimization process that allows hotels to maximize their revenues. In this vein, this study adapts the online transient hotel demand functions to deterministic and stochastic dynamic models—two extended optimal pricing methods existing in the literature—in order to determine the prices that maximize the revenues of two resort hotels located in Majorca. The main findings indicate that (1) seasonality, the number of rooms available, the hotel location, and the tourist profile affect dynamic pricing (DP); (2) the booking horizon limitation leads to larger revenue decreases under elastic demand; (3) higher levels in demand elasticities generally produce lower levels of prices; and (4) the distribution of elasticities across the booking horizon and the natural variability of demand have an impact on DP. Implication for industry revenue managers is that they have to consider the booking horizon duration together with the demand price sensitivity in order to maximize the hotel revenues.

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