Rob Hyndman has a great post on his blog with example on how to cross-validate a time series model. The basic concept is simple: You start with a minimum number of observations (k), and fit a model (e.g. an arima model) to those observation...

fMRI data from 90 locations in the brain look somewhat like daily closing prices on 116 stocks if you squint just right. Marginal Revolution was nice enough to point to “Topological isomorphisms of human brain and financial market networks”. I’ve only just glanced through the paper. I find it interesting, but I’m fairly skeptical. The … Continue reading...

Revolution Analytics' Joe Rickert has a new post on inside-R.org, demonstrating how you can use R and the RevoScaleR package to extract time series data from time-stamped logs (in this case, the "US Domestic Flights From 1990 to 2009" dataset on Infochimps): Analyzing time series data of all sorts is a fundamental business analytics task to which the R...

I was recently asked how to implement time series cross-validation in R. Time series people would normally call this “forecast evaluation with a rolling origin” or something similar, but it is the natural and obvious analogue to leave-one-out cross-validation for cross-sectional data, so I prefer to call it “time series cross-validation”. Here is some example

DataMarket, a portal that provides access to more than 14,000 data sets from various public and private sector organizations, has more than 100 million time series available for download and analysis. (Check out this presentation for more info about DataMarket.) And now with the new package rdatamarket, it's trivially easy to import those time series into R for charting,...

I’ll be giving a talk on Forecasting time series using R for the Melbourne Users of R Network (MelbURN) on Thursday 27 October 2011 at 6pm. I will look at the various facilities for time series forecasting available in R, concentrating on the forecast package. This package implements several automatic methods for forecasting time series

Time series data are widely seen in analytics. Some examples are stock indexes/prices, currency exchange rates and electrocardiogram (ECG). Traditional time series analysis focuses on smoothing, decomposition and forecasting, and there are many R functions and packages available for those … Continue reading →