We sum up the methodology of the team tololo for the Global Energy Forecasting Competition 2012: Load Forecasting. Our strategy consisted of a temporal multi-scale model that combines three components. The first component was a long term trend estimated by means of non-parametric smoothing. The...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

In this paper we study two methodologies which identify and specify canonical form VARMA models. The two methodologies are: (i) an extension of the scalar component methodology which specifies canonical VARMA models by identifying scalar components through canonical correlations analysis and...

The vector innovation structural time series framework is proposed as a way of modelling a set of related time series. Like all multi-series approaches, the aim is to exploit potential inter-series dependencies to improve the fit and forecasts. A key feature of the framework is that the series...

The Beveridge Nelson vector innovation structural time series framework is new formu- lation that decomposes a set of variables into their permanent and temporary components. The framework models inter-series relationships and common features in a simple man- ner. In particular, it is shown that...