Nonlinear and Stochastic Dynamic Models

2013 ◽  
pp. 489-574
Keyword(s):  
Dynamic Models ◽  
Stochastic Dynamic

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.

scholarly journals Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Cheng-Wei Li ◽  
Bor-Sen Chen
Keyword(s):  
Regulatory Networks ◽  
Dynamic Models ◽  
Environmental Stresses ◽  
Protein Networks ◽  
Stochastic Dynamic ◽  
Core Network ◽  
Protective Mechanisms ◽  
Living Organisms ◽  
Final Survival ◽  
Functional Mechanisms

Cellular responses to sudden environmental stresses or physiological changes provide living organisms with the opportunity for final survival and further development. Therefore, it is an important topic to understand protective mechanisms against environmental stresses from the viewpoint of gene and protein networks. We propose two coupled nonlinear stochastic dynamic models to reconstruct stress-activated gene and protein regulatory networks via microarray data in response to environmental stresses. According to the reconstructed gene/protein networks, some possible mutual interactions, feedforward and feedback loops are found for accelerating response and filtering noises in these signaling pathways. A bow-tie core network is also identified to coordinate mutual interactions and feedforward loops, feedback inhibitions, feedback activations, and cross talks to cope efficiently with a broader range of environmental stresses with limited proteins and pathways.

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