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

(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!

That's very zen, but ultimately it doesn't answer my question.

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.