Abstract Data assimilation (DA) aims to optimally combine model forecasts and observations that are both partial noisy. Multi‐model DA generalizes the variational or Bayesian formulation of Kalman filter, we prove it is also minimum variance linear unbiased estimator. Here, formulate implement a multi‐model ensemble filter (MM‐EnKF) based on this framework. The MM‐EnKF can multiple ensembles fo...

Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management rebalancing. Probabilistic provides a set of information on uncertainties in forecasting, thus it is suitable use stochastic management. Our research objective to develop probabilistic time-series BSS demand. We an RNN–LSTM-based model, called DeepAR, the station-wis...

Ensemble classifiers combine the classification results of several classifiers. Simple ensemble methods such as uniform averaging over a set of models usually provide an improvement over selecting the single best model. Usually probabilistic classifiers restrict the set of possible models that can be learnt in order to lower computational complexity costs. In these restricted spaces, where inco...

The effects of wind uncertainty on aircraft fuel consumption are analyzed using a probabilistic trajectory predictor. The case of cruise flight subject to an average constant wind is considered. The average wind is modeled as a random variable; the wind uncertainty is obtained from ensemble weather forecasts. The probabilistic trajectory predictor is based on the Probability Transformation Meth...