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I am looking for a technical solution to store economic and financial timeseries (nothing intraday for now, just daily/weekly/yearly)

  1. Most timeseries database I find do not seem to take into account the fact that timeseries can be revised. For instance Industrial Production for Sept might be revised 2 months after the initial release once the administration has reviewed all the information they receive. For that reason we need 2 time dimensions: the as of date and the publication date, so that we are able to do time travel.

  2. I also would like to be able to tag properly each timeseries with meta data (unit, source, product, etc) so that my timeseries are properly organised and searchable

thank you for your suggestions

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3 Answers 3

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There are many ways to deal with this.

One is to model the data with the following (sample) schema, which can be done with pretty much any database:

  • series_id
  • date - this is the reference period for an observation
  • start_dt - this is when the observation for date becomes available
  • to_dt (optional) - this is when an observation for date is no longer active
  • value - this is the value for period date

To get a time series as of a particular vintage date $t$, you simply retrieve all the observations with a start_dt that's less than or equal to $t$ and then filter for the last observation for each date. This method is pretty efficient from a storage perspective (because you're mostly saving the deltas in the time series).

An alternative is to look at the VersionStore of arctic, which can deal with this very gracefully as well. You can definitely attach metadata of your choice to each series.

A good reference is Developing Time-Oriented Database Applications in SQL, which has very detailed coverage for time series revisions.

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The only Time Series data bases which I know of are similar to Rob Hyndman's R repository which is a database of finalized figures for modeling experiments, the only public one which I know that is available and is revisable, is Quandl which has a very large amount of free and paid private ,commercial & industrial, financial and government data. You could try contacting Quandl just do a google search. Otherwise I can offer no other ideas.

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As for the data model, I'm using RDF in conjunction with industry vocabulary and ontologies. Most data is published in RDF anyway.

As for the database, any triplestore or graph database should do. I'm using OpenLink's Virtuoso.

To your points:

  1. AGLDWG dataset ontology provides vocabulary for this use case. More generically you could also use pav or just roll your own terms.
  2. Same vein as 1. I use qudt for units and skos to build hierarchies.
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