Multiscale Decomposition of Big Data Time Series for Analysis and Prediction of Macroeconomic Data: A Recent Approach | Chapter 04 | Theory and Applications of Mathematical Science Vol. 3
The
problem of the extraction of the relevant information for pre- diction 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 enter- tained models. In addition, two
other drawbacks should be considered: firstly, humandriven 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 time scale 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 crosscorrelation functions (whose employment will be
theoretically justified) and a suitable objective function.
Author(s) Details
Livio Fenga
ISTAT, Italian National
Institute of Statistics, Italy.
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