How do I analyse seasonality and trends with iDBQuery?
Ask iDBQuery 'Is our sales growth a real trend or just seasonality?' It separates the underlying trend from recurring seasonal patterns in your history, returns both as a chart, and cites each period's figure back to the source rows so you can tell signal from noise.
Seasonality is a repeating pattern tied to the calendar, weekday dips, summer slumps, December peaks, while a trend is the underlying direction once that pattern is removed. Confusing the two leads to bad decisions. iDBQuery helps you separate them from your own data.
- You'd ask iDBQuery: 'Show monthly sales for the last three years, highlight the recurring seasonal pattern, and tell me the underlying trend once seasonality is accounted for.'
- It decomposes the series into trend and seasonal components, returns a chart of both, and provides the figures in a table.
- Each period is cited to its source rows, so the analysis is grounded, not a smoothed illusion.
The useful follow-ups come fast: 'Which months are reliably strongest?' or 'Is this quarter genuinely up, or just its usual seasonal peak?' iDBQuery keeps the thread and answers in context. Because it is transparent about method and shows its SQL, an analyst can adjust the seasonal window. This underpins better forecasting: understanding seasonality is what makes a sales or demand forecast believable. Save a live report so the trend refreshes on new data, and let the Analyst agent flag when the underlying trend, not just the seasonal wave, actually changes direction, with the rows behind the call.
Updated 2026-06-22