Autonomous agents functioning in complex and rapidly changing environments can improve their task performance if they update and correct their world model over the life of the agent. Existing research on this problem can be divided into two classes. First, reinforcement learners that use weak inductive methods to directly modify an agent’s procedural execution knowledge. These systems are robus...

The errors in the first-guess (forecast field) of an analysis system vary from day to day, but, as in all current operational data assimilation systems, forecast error covariances are assumed to be constant in time in the NCEP operational three-dimensional variational analysis system (known as a spectral statistical interpolation or SSI). This study focuses on the impact of modifying the error ...

In this paper, we showed a method to forecast the daily stock price using neural networks and the result of the Neural Network forecast is compared with the Statistical forecasting result. Stock price prediction is one of the emerging field in neural network forecasting area. This paper also presents the Neural Networks ability to forecast the daily Stock Market Prices. Stock market prediction ...

In the present age of globalization, technology-revolution and sustainable development, the presence of seasonality in tourist arrivals is considered as a key policy issue that affects the global tourism industry by creating instability in the demand and revenues. The seasonal component in a time-series distorts the prediction attempts for policy-making. In this context, it is quintessential to...

The properties of the multiple-scale instabilities present in a non-hydrostatic forecast model are investigated. The model simulates intense convection episodes occurring in northern Italy. A breeding technique is used to construct ensembles of perturbations of the model trajectories aimed at representing the instabilities that are responsible for error growth on various timescales and space sc...

Preview: Some traditional measurements of forecast accuracy are unsuitable for intermittent-demand data because they can give infinite or undefined values. Rob Hyndman summarizes these forecast accuracy metrics and explains their potential failings. He also introduces a new metric—the mean absolute scaled error (MASE)—which is more appropriate for intermittent-demand data. More generally, he be...

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption electricity France 2017. Because that, detecting anomalies (outliers) is crucial order to identify both potential opportunities reduce and malfunctioning metering system. This work aims compare performance several outlier detection methods, such classical statistical methods (as boxpl...

Numerical models of the atmosphere are based on best theory available. Understandably, theoretical assessment errors induced by use such is confounding. Without clear guidance, experimental separation model-induced part total forecast error also challenging. In this study, and ensemble perturbation variances were decomposed. Smaller- larger-scale components, separated as a function lead time, i...