Applicapability of Statistic Time Series Analysis for Stratigraphy

The events from the end-Permian to the early Triassic have been thoroughly documented with a wealth of precise, high-resolution stratigraphic data. However, many ideas and opinions have been proposed without mathematically acceptable links, with different studies citing various reasons. In the context of paleontological data, this condition will become evident due to the inadequate application of objective methods in establishing a link between the proposed cause and the examined paleontological outcomes. The speaker has undergone rigorous testing of a variety of methods, including “Mathematical Analysis”, “Multiple Regression Analysis”, “Multivariate Statistical Analysis” and “Statistic Time Series Analysis.” “Mathematical Analysis” (MA) is applicable to cause-and-effect data sets, such as meteorological phenomena, for which the physical process is fully understood. The majority of stratigraphic data is unable to determine a physical perfect relationship between causes and resultant data. For this reason, this method is generally not applicable. “Multiple Regression Analysis” (MRA) is a powerful tool in paleoceanography and paleoclimatology that can be used to estimate paleo-temperature with the aid of faunal compositional changes. MA and MRA can be applied if resultant data exhibits a positive or negative linear relationship to the causes in question. This method, however, is not related with time or accidental irregular phenomena. MRA can be generalized to “Multivariate Statistical Analysis” (MSA). A number of MSA methods have been developed and implemented in statistical software applications. MSA is suitable for complex data sets, but the issue in MRA is also the issue in MSA. One of the most effective methods is called “Statistic Time Series Analysis” (STSA). The STSA has undergone extensive development within the fields of financial economics. Unpredictive changes over time and uncertainty regarding the cause of changes are quite similar to stratigraphic data. The STSA demonstrates a clear advantage in circumstances that necessitate mathematical objective analysis for the establishing a link between cause and effect, and evaluating the degree of effectiveness. In this presenation, some topics elated to the STSA are presented for stratigraphy [This study is supported by the NSFC (Grant No. 42230205)].