Univariate Time Series in Geosciences

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Bergson was apparently too unintelligent to understand time dilation, but seemingly delusional enough about his abilities that he believed he did understand. Bergson managed to convince the Nobel Committee that time dilation does not occur. And so the greatest achievement of one of the greatest scientists who ever lived went unacknowledged by the Nobel Committee.

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The reason, it seems, is that a blogger, Lucia Liljegren , has managed to convince them. Liljegren is in some ways like Bergson: ignorant, unintelligent, and delusional, but nonetheless influential. Specifically, for ARIMA time series, the variance becomes arbitrarily large, over long enough time, whereas for the climate system, the variance does not become arbitrarily large. In other words, when we consider any statistical model , we will find something wrong with the model. Thus, when considering a model, the question is not whether the model is wrong—because the model is certain to be wrong.

Rather, the question is whether the model is useful, for a particular application. This is a fundamental issue that is commonly taught to undergraduates in statistics. Yet Liljegren ignores it. As an illustration, consider a straight line with noise as a model of global temperatures.

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Such a line will become arbitrarily high, over long enough time: e. Global temperatures, however, will not become arbitrarily high.


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Hence, the model is wrong. In fact, a straight line is commonly used for temperatures, because everyone understands that it is to be used only over a finite time e. Over a finite time, the line cannot become arbitrarily high; so, the argument against using a straight line fails. You might ask why Liljegren would be involved in this, if she has such poor understanding.

Perhaps the answer lies in psychology. In psychology, there is a well-known syndrome whereby someone is so incompetent at a task that they genuinely do not realize that they are incompetent; the syndrome is known as the Dunning—Kruger effect. This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!

Nonlinear multivariate and time series analysis by neural network methods. Read more.


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    New Releases. Description This is a detailed introduction to the statistical analysis of geophysical time series, using numerous examples and exercises to build proficiency. The exercises lead the reader to explore the meaning of concepts such as the estimation of the linear time series AMRA models or spectra. The book also serves as a guide to using the open-source "R" program for statistical analysis of time series.