Forecasting, Statistics, and Data Mining Texts

Elements of Statistical Learning, Data Mining and Prediction, 2nd edition, 10th printing, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, downloadable PDF file.

An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, downloadable PDF file. I highly recommend this book, and am pretty sure you are going to order one from Springer after looking at the PDF file. It’s a keeper.

Forecasting: principles and practice, Rob J Hyndman and George Athanasopoulos, an online book. You need to look this over, make sure you can master the basics presented here. Constantly revised and updated by the authors.

StatSoft electronic statistics book, good basic introduction to applied statistics, pairs well with StatSoft statistics software (STATISTICA)

Data Warehouses

Federal Reserve Economic Data (FRED), Federal Reserve Bank of St. Louis. Download, graph, and track 83,000 US and international time series from 57 sources.

International Monetary Fund World Economic Outlook (WEO) database, updated twice yearly.

DataMarket, Find, understand and share data, an open portal to thousands of datasets from leading global providers. Explore, upload your own data, create beautiful visualizations  and reports in seconds – it’s free!

Specialized Data Repositories


UC Irvine (UCI) Machine Learning Repository. Site maintains hundreds of  data sets as a service to the machine learning community.

M Competitions Data from International Institute of Forecasters. You have to click on the specific competition and follow through to the place where it’s possible to download the Competition time series, as well as the forecasts from the Competition. Site also links to Rob Hyndman’s Time Series Library, now maintained by DataMarket.

Graphs and Visuals

Macrotrends scores of interactive, frequently updated charts relating to macroeconomic and global economic variables.


One thought on “Resources”

  1. Hi,

    Great site. You have lots of great learning resources, I just wanted to add some additional ways of learning R online:

    DataCamp teaches R in the comfort of your browser with video lessons and coding exercises. A free interactive R tutorial is provided.

    Data Science Central:
    Quick start-up kit for R and data science.

    Another interactive R tutorial, used in Coursera courses.

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Sales and new product forecasting in data-limited (real world) contexts