2 ms·

Anytime you’re interested in seeing how things change over time, that’s time series. It’s a very big category of use cases.

• throwaway894345 4y ago
  Sure, but analytics is sometimes change over time, and other times change over some other dimension. Presumably if time is just one dimension among many, then timeseries is probably not the right fit in general?
  • epgui 4y ago
    As with anything else, you can approach specific problems in many different ways.
    • throwaway894345 4y ago
      That's very zen, but ultimately it doesn't answer my question.
      • epgui 4y ago
        Well, I could be more opinionated, but even in very specific situations, reasonable people disagree about the best way to model data, and I don't really know a lot about your specific problem-space or situation. My personal preference is to think of almost any changing measurement or event stream as a time series. See also the reply to a sibling comment.
    • slt2021 4y ago
      timeseries is usually specific to use cases when you data represents some signal over time, like temperature reading, stock price, etc. so you need 2 components: timestamp and signal reading, in this case all specific timeseries analytics apply: sliding/tumbling window, avg per window, smoothing, autocorrelation and all other techniques from Digital Signal Processing/timeseries analytics. Your regular monthly Sales data of ACME Corp by product category and storeId - this is not timeseries, just general BI
      • ryanbooz 4y ago
        (NB - post author) Great definition! Having worked for years on both energy and IoT applications, the argument here is that your "monthly sales data" is likely being aggregated from your time-series data (sales transactions over time). If you store the transaction data in a database like TimescaleDB, then continuous aggregates provide the straightforward method for keeping that aggregated, monthly sales data up-to-date. :-D!