The Time Series Data Library is a collection of about 800 time series that I have maintained since about 1992, and hosted on my personal website. It includes data from a lot of time series textbooks, as well as many other series that I’ve either collected for student projects or helpful people have sent to me.
I’ve now moved the collection onto DataMarket which provides much better facilities for maintaining and using time series data. You can easily search the collection, graph any series, filter by seasonal period, and so on. You can also export data in many formats. Each data set has its own short link; for example, the famous Canadian lynx data is at http://data.is/Ky69xY.
One particularly useful feature is the ability to read directly into R using the rdatamarket package. All you need to know is the short link. For example, to download “Deaths from gun-related homicides in Australia, 1915–2004″, use the following R code
library(rdatamarket) deaths <- dmseries("http://data.is/Ky6vVf")
The data is set to
zoo class. To make it of
ts class, use
deaths <- as.ts(deaths[,1])
In this case,
deaths only contained one column, but in general multivariate time series can be downloaded in this manner.
DataMarket contains thousands of other time series from organizations including Eurostat, the IMF, the United Nations, Gapminder, and many more. Some time series require a subscription, but many can be used freely. The time series in the TSDL will remain freely available.
I’m grateful to DataMarket for agreeing to host my library without charge, and I encourage everyone interested in time series analysis to check them out.
If you use any data from the TSDL in a publication, please use the following citation:
Hyndman, R.J. Time Series Data Library, http://data.is/TSDLdemo. Accessed on <insert date here>.
The data files will remain on my website so that existing links will not be broken.
- More time series data online
- Time series data in R
- A new R package for detecting unusual time series
- Cyclic and seasonal time series
- Fitting models to short time series