571 search results for "eof"

meteoForecast 0.43: GFS, NAM, and RAP included

October 20, 2014
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meteoForecast 0.43: GFS, NAM, and RAP included

Some months ago I published the meteoForecast package, with functions to download data from the Meteogalicia and OpenMeteo NWP-WRF services. …Sigue leyendo →

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PCA / EOF for data with missing values – a comparison of accuracy

September 15, 2014
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PCA / EOF for data with missing values – a comparison of accuracy

Not all Principal Component Analysis (PCA) (also called Empirical Orthogonal Function analysis, EOF) approaches are equal when it comes to dealing with a data field that contain missing values (i.e. "gappy"). The following post compares several methods by assessing the accuracy of the derived PCs to reconstruct the "true" data set, as was similarly...

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meteoForecast, a package to obtain NWP-WRF forecasts in R

meteoForecast, a package to obtain NWP-WRF forecasts in R

The Weather Research and Forecasting (WRF) Model is a numerical weather prediction (NWP) system. NWP refers to the simulation and …Sigue leyendo →

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DINEOF (Data Interpolating Empirical Orthogonal Functions)

October 30, 2012
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DINEOF (Data Interpolating Empirical Orthogonal Functions)

I finally got around to reproducing the DINEOF method (Beckers and Rixon, 2003) for optimizing EOF analysis on gappy data fields - it is especially useful for remote sensing data where cloud cover can result in large gaps in data. Their paper gives a nice overview of some of the various methods...

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Empirical Orthogonal Function (EOF) Analysis for gappy data

November 24, 2011
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Empirical Orthogonal Function (EOF) Analysis for gappy data

The following is a function for the calculation of Empirical Orthogonal Functions (EOF). For those coming from a more biologically-oriented background and are familiar with Principal Component Analysis (PCA), the methods are similar. In the climate sciences the method is usually used for the decomposition of a data field into dominant spatial-temporal modes. Read...

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RStudio Connect 1.5.0 – Introducing Tags!

May 23, 2017
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RStudio Connect 1.5.0 – Introducing Tags!

We’re excited to announce a powerful new ability to organize content in RStudio Connect: version 1.5.0. Tags allow publishers to arrange what they’ve published and enable users to find and discover the content most relevant to them. The release also includes a newly designed (and customizable!) landing page and multiple important security enhancements. Tagging Content

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The Marcos Lopez de Prado Hierarchical Risk Parity Algorithm

May 22, 2017
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The Marcos Lopez de Prado Hierarchical Risk Parity Algorithm

This post will be about replicating the Marcos Lopez de Prado algorithm from his paper building diversified portfolios that outperform … Continue reading →

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Training Neural Networks with Backpropagation. Original Publication.

May 17, 2017
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Training Neural Networks with Backpropagation. Original Publication.

Neural networks have been a very important area of scientific study that has evolved by different disciplines such as mathematics, biology, psychology, computer science, etc.The study of neural networks leapt from theory to practice with the emergence of computers.Training a neural network by adjusting the weights of the connections is computationally very expensive so its...

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Network analysis of Game of Thrones family ties

May 14, 2017
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Network analysis of Game of Thrones family ties

In this post, I am exploring network analysis techniques in a family network of major characters from Game of Thrones. Not surprisingly, we learn that House Stark (specifically Ned and Sansa) and House Lannister (especially Tyrion) are the most import...

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How not to make an evergreen review graph

May 13, 2017
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How not to make an evergreen review graph

In this post I am inspired by two tweets, mainly this one and also this one. Since the total number of articles every year is increasing, no matter which subject you choose, the curve representing number of articles as a function of year of publication...

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