Learning R Andrew Redd R Blog Statistical Modeling, Causal Inference, and Social Science Rob J Hyndman The latest issue of the IJF is a bumper issue with over 500 pages of forecasting insights. The GEFCom2014 papers are included in a special section on probabilistic energy forecasting, guest edited by Tao Hong and Pierre Pinson. This is a major milestone in energy forecasting research with the focus on probabilistic forecasting and forecast evaluation done using a quantile scoring method. Only a few years ago I was having to explain to energy professionals why you couldn’t use a MAPE to evaluate a percentile forecast. With this special section, we now have a tutorial review on probabilistic electric load forecasting by Tao Hong and Shu Fan, which should help everyone get up to speed with current forecasting approaches, evaluation methods and common misunderstandings. The section also contains a large number of very high quality articles showing how to do state-of-the-art density forecasting for electricity load, electricity price, solar and wind power.
RStudio in the cloud, for dummies You can have your own cloud computing version of R, complete with RStudio. Why should you? It's cool! Plus, there's a lot more power out there than you can easily get on your own hardware. And, it's R in a web page. This entry is largely made possible by the work of Louis Alsett, who's completing his doctoral work at Trinity College, University of Dublin. Start-up1. 2. 3. a. 1) click "Create a New Security Group". 2) Give a name (like "RStudio") and 3) a description ("RStudio")-- both are required. g. Use4. 5. 6. 7. There's your R in the cloud! ManagementOur understanding of the "Free Usage Tier" is that you can leave this on all the time for a year without incurring any charges. However, there is also a "stopped" state. As long as an instance is running, you retain all aspects of your session-- it's just as if you had a computer that you left on running RStudio all the time. Happy cloud computing! * The free usage is limited to "micro" instances, such as we use here.
RStudio Blog Statistics, R, Graphics and Fun | Yihui Xie Graph of the Week R: Retrieving information from google using the RCurl package « "R" you ready? R: Retrieving information from google using the RCurl package 01Jan09 Lately I read the article Automatic Meaning Discovery Using Google by Cilibras and VitanyiIt which introduces the normalized google distance (NGD) as a measure of semantic relatedness of two search terms. Now I want to figure out how to impelement this calculation using R. I found a nice site written by Duncan Temple Lang that explains the extraction of HTML code from any internet site using the RCurl package. library(RCurl) # now lets extract the HTML code from my blog using getURL() # from the RCurl package getURL(" # this looks pretty unstructured. The above implementation surely is not technically mature (e.g. the extraction code). As a last step let’s wrap the above way to extract the google search results count into a function. Next time I will use this function to calculate the normalized google distance. Happy New Year! Mark Like this: Like Loading...
Forecasting Welcome to our online textbook on forecasting. This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details. The book is written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area; (2) undergraduate students studying business; (3) MBA students doing a forecasting elective. We use it ourselves for a second-year subject for students undertaking a Bachelor of Commerce degree at Monash University, Australia. For most sections, we only assume that readers are familiar with algebra, and high school mathematics should be sufficient background. Use the table of contents on the right to browse the book.
Win Vector Old tails: a crude power law fit on ebook sales We use R to take a very brief look at the distribution of e-book sales on Amazon.com. Read more… You don’t need to understand pointers to program using R Practical Data Science with R: Release date announced It took a little longer than we’d hoped, but we did it! If you haven’t yet, order it now! (softbound 416 pages, black and white; includes access to color PDF, ePub and Kindle when available) Can a classifier that never says “yes” be useful? Many data science projects and presentations are needlessly derailed by not having set shared business relevant quantitative expectations early on (for some advice see Setting expectations in data science projects). Categories: data science, Opinion, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags: classifier quality, deviance, Entropy, likelihood, log-likelihood Some statistics about the book The Statistics behind “Verification by Multiplicity”
Journal of Statistical Software