Abstract
This paper proposes the Bayesian analysis method (BAM) to classify the time series data which derives the complicated phenomena in the international greenhouse gas emissions trading. Our investigation compared the results using the method of Discrete Fourier transform (DFT) and BAM. Such comparisons have revealed the following implications: (1) BAM is superior to DFT in terms of classifying time series data by the different distances; and (2) the different distances in BAM show the importance of 1% influence of emission reduction targets.
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Nakada, T., Takadama, K., Watanabe, S. (2011). Bayesian Analysis Method of Time Series Data in Greenhouse Gas Emissions Trading Market. In: Chen, SH., Terano, T., Yamamoto, R. (eds) Agent-Based Approaches in Economic and Social Complex Systems VI. Agent-Based Social Systems, vol 8. Springer, Tokyo. https://doi.org/10.1007/978-4-431-53907-0_11
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DOI: https://doi.org/10.1007/978-4-431-53907-0_11
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