Theory and Applications of Mathematical Science Vol. 3

Abstract

The problem of the extraction of the relevant information for prediction purposes -in a Big Data time series context -is tackled. This issue is especially crucial when the forecasting activity involves macroeconomic time series, i.e. when one is mostly interested in finding leading variables and, at the same time, avoiding overfitted model structures. Unfortunately, the use of big data can cause dangerous overparametrization phenomena in the entertained models. In addition, two other drawbacks should be considered: firstly, human-driven handling of big data on a case-by-case basis is an impractical (and generally not viable) option and secondly, focusing solely on the raw time series might lead to suboptimal results. The presented approach deals with these problems using a twofold strategy: i) it expands the data in timescale domain, in the attempt to increase the likelihood of giving emphasis to possibly weak, relevant, signals and ii) carries out a multi-step dimension reduction procedure. The latter task is done by means of cross-correlation functions (whose employment will be theoretically justified) and a suitable objective function.