Abstract. The main purpose of this article is to emphasize the importance clarifying probabilistic framework adopted for volcanic hazard and eruption forecasting. Eruption forecasting analysis seek quantify deep uncertainties that pervade modeling pre-, sin-, post-eruptive processes. These can be differentiated into three fundamental types: (1) natural variability systems, usually represented a...

Abstract Support vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that can describe the dynamic characteristics of financial processes make more accurate forecasts than other machine learning techniques. The first main contribution this paper to propose methodology modelling forecasting...

Recent research carried out in CRES has produced a data-based modelling approach to the adaptive forecasting of river flow or levels using rainfall or upstream flow inputs as leading indicators. This approach, which is based on the estimation of a general transfer function model from historical data, has significant advantages over existing flood forecasting methods: for example, on-line adjust...

This paper aims models electricity load curves for short-term forecasting purposes. A broad class of multivariate dynamic regression model is proposed to model hourly electricity load. Alternative forecasting models, special cases of our general model, include separate time series regressions for each hour and week day. All the models developed include components that represent trends, seasons ...