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The recent development of smart meters has allowed the analysis of household electricity consumption in real time. Predicting electricity consumption at such very low scales should help to increase the efficiency of distribution networks and energy pricing ...
Accurate river flow forecasting is an important asset for stream and reservoir management, being often translated into substantial social, economic and ecological gains. This contribution aims at coupling satellite rainfall estimates and machine learning t ...
Coordination of decentralized supply chains using contract design is a problem that has been widely addressed in the literature. We consider a divergent supply chain including a supplier and several retailers producing fashion products with short sale seas ...
Increasing environmental awareness and energy costs encourage the increase of the contribution of renewable energy sources (RES) to the energy supply of buildings. However, the integration of RES and energy storage systems introduces significant challenges ...
Do laboratory subjects correctly perceive the dynamics of a mean-reverting time series? In our experiment, subjects receive historical data and make forecasts at different horizons. The time series process that we use features short-run momentum and long-r ...
Pedestrian modeling is a tool for designing and optimizing infrastructures. In particular, walking in transportation hubs such as railway stations and airports is the key for an efficient multimodal transport systems. Data collections are needed in estimat ...
Prof Bierlaire will present the powerful methods of Discrete Choice Analysis that are used in forecasting human behaviour to predict, among others, the demand for a new product under alternative pricing strategies. From individual behaviors, such technique ...
Singular spectrum analysis is a natural generalization of principal component methods for time series data. In this paper we propose an imputation method to be used with singular spectrum-based techniques which is based on a weighted combination of the for ...
This study analyses the use of neural networks to produce accurate forecasts of total bookings and cancellations before departure, of a major European rail operator. Effective forecasting models, can improve revenue performance of transportation companies ...
This paper presents an information--theoretical method for weighting ensemble forecasts with new information. Weighted ensemble forecasts can be used to adjust the distribution that an existing ensemble of time series represents, without modifying the valu ...