New results in forecasts combination using PCA
with interval time series: The case of oil price
?
Carlos Maté1, , Andrea Vašeková2
1. Universidad Pontificia Comillas, Madrid (Spain)
2. Masaryk University, Brno (Czech Republic)
? Contact author: cmate@icai.comillas.edu
Keywords: oil price forecasting, principal component analysis, symbolic data analysis
This century is much more complex, uncertain and riskier than the previous one. In
addition to the global crisis which began in December 2007 and still remains around the
world, the year 2015 has brought new problems like the new oil crisis or the China crisis,
among others. In this scenario, having accurate forecasts in economics, finance, energy,
health and so on is more critical than ever.
According to Energy Information Administration, oil is the most consumed source of
energy, and thus most of economic activity depends on the evolution of oil prices. Lately
it has been observed that the oil prices were very volatile. Alquist et al. (2013) made a
critical survey regarding the econometric models (time series models, financial models
and structural models) used to predict the oil prices. A very recent review on artificial
intelligence methods in oil price forecasting can be found in Sehgal and Pandey (2015).
The introduction of interval time series (ITS) concepts and forecasting methods has been
proposed in various papers, such as Arroyo et al. (2011), Arroyo and Maté (2006), among
others.
After more than 40 years of research, there is a general consensus that "combining forecasts
reduces the final forecasting error" (see, for example, Clemen (1989) and Timmerman
(2006)). As part of this consensus there is also a well-known fact that "a simple average of
several forecasts often outperforms complicated weighting schemes" which was named
the forecast combination puzzle by Stock and Watson (2004).
Very recently, Maté (2015) has proposed several combination schemes with interval time
series (ITS) forecasts. In addition, the forecast combination puzzle in the ITS forecasts
framework has been analyzed in the context of different accuracy measures. As one result
of this paper, the forecast combination puzzle remains in the case of ITS. As another
result, the principal component analysis (PCA) method for interval-valued data has been
proposed as a page of an agenda for future research.
PCA was one of the first methods extended from single data to interval-valued data. For
a review paper on this research field, see Douzal-Chouakria et al. (2011).
In this paper we develop a new ITS forecast combination method based on PCA. We will
show how this method performs in the forecast combination puzzle. As a case study we
analyze the oil market. Further research issues will be proposed.
References
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Arroyo, J., R. Espínola, and C. Maté (2011). Different approaches to forecast interval time
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Arroyo, J. and C. Maté (2006). Introducing interval time series: accuracy measures. In
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