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International audience; Research on the analysis of time series has gained momentum in recent years, as knowledge derived from time series analysis can improve the decision-making process for industrial and scientific fields. Furthermore, time series analysis is often an essential part of business intelligence systems. With the growing interest in this topic, a novel set of challenges emerges. Utilizing forecasting models that can handle a large number of predictors is a popular approach that can improve results compared to univariate models. However, issues arise for high dimensional data. Not all variables will have direct impact on the target variable and adding unrelated variables may make the forecasts less accurate. Thus, the authors explore methods that can effectively deal with time series with many predictors. The authors discuss state-of-the-art methods for optimizing the selection, dimension reduction, and shrinkage of predictors. While similar research exists, it exclusively targets small and medium datasets, and thus, the research aims to fill the knowledge gap in the context of big data applications.

Forecasting models that utilize multiple predictors are gaining popularity in a variety of fields. In some cases they allow constructing more precise forecasting models, leveraging the predictive potential of many variables. Unfortunately, in practice we do not know which observed predictors have a direct impact on the target variable. Moreover, adding unrelated variables may diminish the quality of forecasts. Thus, constructing a set of predictor variables that can be used in a forecast model is one of the greatest challenges in forecasting. We propose a new selection model for predictor variables based on the directed causality graph and a modification of the random walk with restarts model. Experiments conducted using the two popular macroeconomics sets, from the US and Australia, show that this simple and scalable approach performs well compared to other well established methods.

International audience; Forecasting the photovoltaic energy power is useful for optimizing and controling the system. It aims to predict the power production based on internal and external variables. This problem is very similar to the one of multiple time series forecasting problem. With the presence of multiple predictor variables, not all of them will equally contribute to the prediction. The goal is, given a set of predictors, to find what is the best / most accurate subset (s) leading to the best forecast. In this work, we present a feature selection and model matching framework. The idea is that we try to find the optimal combination of forecasting model with the most relevant features for given variable. We use a variety of causality based selection approaches and dimension reduction techniques. The experiments are conducted on real data and the results advocate the usefulness of the proposed approach.