Uncertainty propagation in the model web:a case study with e-Habitat

eHabitat is a Web Processing Service (WPS) designed to compute the likelihood of finding ecosystems with equal properties. Inputs to the WPS, typically thematic geospatial "layers", can be discovered using standardised catalogues, and the outputs tailored to specific end user needs. Because these layers can range from geophysical data captured through remote sensing to socio-economical indicators, eHabitat is exposed to a broad range of different types and levels of uncertainties. Potentially chained to other services to perform ecological forecasting, for example, eHabitat would be an additional component further propagating uncertainties from a potentially long chain of model services. This integration of complex resources increases the challenges in dealing with uncertainty. For such a system, as envisaged by initiatives such as the "Model Web" from the Group on Earth Observations, to be used for policy or decision making, users must be provided with information on the quality of the outputs since all system components will be subject to uncertainty. UncertWeb will create the Uncertainty-Enabled Model Web by promoting interoperability between data and models with quantified uncertainty, building on existing open, international standards. It is the objective of this paper to illustrate a few key ideas behind UncertWeb using eHabitat to discuss the main types of uncertainties the WPS has to deal with and to present the benefits of the use of the UncertWeb framework.

Does money matter in inflation forecasting?

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regressiontechniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a nave random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists' long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies. © 2010 Elsevier B.V. All rights reserved.

A novel ranking procedure for forecasting approaches using Data Envelopment Analysis

To compare the accuracy of different forecasting approaches an error measure is required. Many error measures have been proposed in the literature, however in practice there are some situations where different measures yield different decisions on forecasting approach selection and there is no agreement on which approach should be used. Generally forecasting measures represent ratios or percentages providing an overall image of how well fitted the forecasting technique is to the observations. This paper proposes a multiplicative Data Envelopment Analysis (DEA) model in order to rank several forecasting techniques. We demonstrate the proposed model by applying it to the set of yearly time series of the M3 competition. The usefulness of the proposed approach has been tested using the M3-competition where five error measures have been applied in and aggregated to a single DEA score.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.