As with anything else, you can approach specific problems in many different ways.
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
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!
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.