GDP is measured with error. But data uncertainty rarely communicated quantitatively in real-time. An exception are the fan charts for historical growth published by Bank of England. To assess how well understood is, we first evaluate accuracy and compare them models past revisions data. Secondly, to gauge perceptions across a wider set experts, conduct new online survey. Our results call greate...

In Machine learning and pattern recognition, building a better predictive model is one of the key problems in presence big or massive data; especially, if that data contains noisy unrepresentative samples. These types samples adversely affect may degrade its performance. To alleviate this problem, sometimes, it becomes necessary to sample after eliminating unnecessary instances by maintaining u...

reliable estimates of runoff are required as a part of the information sets that help watershed managers make informed decisions on water resources planning and management. this study was carried out in shafaroud watershed located in the north of iran. in order to achieve the best runoff simulation in the study area, first rainfall data of four stations during 1998 to 2011 were collected and co...

Engineering systems are growing in complexity, requiring increasingly intelligent and flexible methods to account for predict uncertainties service. This paper presents a framework dynamic uncertainty prediction under limited data (UPLD). Spatial geometry is incorporated with LSTM networks enable real-time multistep of quantitative qualitative over time. Validation achieved through two case stu...