The premise is fairly simple: Timetric was created to store, share, and analyze data over time. For predicting trends, proving assertions, and recommending actions, time series analysis is a highly valuable tool. It's Facebook's Lexicon all grown up and actually useful, pulling data from all over the web and querying this huge database to serve significant results.
Calling to mind truly hot topics such as Facebook's rumored sentiment analysis engine, the parsing of real-time data, and the need for data over facts in modern journalism, Timetric addresses one facet of a growing gap between what information is now available online and how much we actually know about what that information means.
Here, one of the principals of Inkling, Andrew Walkingshaw, talks about how time series analysis works.
In our recent post on data in journalism, we wrote: "The voracious appetite for data... has been apparent for months. It is less about turning good ideas into stories and more about seeing how data informs our understanding of events happening right now. Each new initiative is another piece of low-hanging fruit picked."
Timetric is one of those low-hanging fruits, gathered with a minimal amount of patience over a period of time and put to great use by any number of entities. With regard to journalism, the Guardian's Data Blog uses Timetric to report on anything from long-term inflation trends to the relative purity of cocaine seized by police.
We ourselves were able to pull in data from a time series library to create charts on the incidence of men with full beards over a number of years:
Once uploaded, data is downloadable and embeddable by users in a number of formats. Users can also designate tags and licenses, and the team has developed a REST-based API for other Timetric-based apps.
Quick visualization and analysis of time-dependent data has never been simpler. We hope that the functions and forms become even richer as Timetric grows.