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研討會總覽

A plea for adaptive data analysis

2014/04/08
  • 研討會日期 : 2014-04-08
  • 時間 : 14:30
  • 主講人 : Professor Norden Huang
  • 地點 : Conference Room B110
  • 演講者簡介 : Professor Huang received his Ph.D. in Fluid Mechanics and Mathematics from the Johns Hopkins University in 1967. He is currently the Director of the Research Center for Adaptive Data Analysis at the National Central University and an Academician of Academia Sinica. He has published extensively on subjects covering data analysis method and its applications to natural science, engineering, biomedical and financial problems.
  • 演講摘要 : Data analysis is indispensable to every scientific endeavors. The existing data analysis methods are all developed by mathematicians based on their rigorous rules. In pursue of the rigor, we are forced to make idealized assumptions and live in a pseudo-real linear and stationary world, in which data analysis is relegated to data processing. But the world we live in is neither stationary nor linear. As scientific research getting increasingly sophistic, the inadequacy of mere processing data becomes glaringly obvious. In fact, the frequency defined from the traditional Fourier analysis can be proved to lack mathematical and physical meanings. To get the truth containing in the data, we have to break away from these limitations; we should let data speak for themselves so that the results could reveal the full range of consequences of nonlinearity and nonstationarity. To do so, we need new paradigm of data analysis methodology without a priori basis to fully accommodating the variations of the underlying driving mechanisms. The solution lies in adaptive data analysis approach. One example is the Empirical Mode Decomposition method and the associated extensions of time-frequency representation. We will also show that, with the adaptive method, we can also determine trend objectively. This new approach is totally different from the traditional parametric or non-parametric methods. Example from GDP data will be used to illustrate the power of the Adaptive Data Analysis methods.