Find Your Real Seasonal Pattern And How Big It Is

How it works

The analysis parses your dates, aggregates duplicates by sum, infers the cadence (daily, weekly, monthly) from the median date gap, and fills small gaps by linear interpolation, every adjustment stated in the report. It then runs an STL decomposition (seasonal-trend decomposition using loess with a periodic seasonal window), splitting the series into trend, seasonal, and remainder. Seasonal strength is max(0, 1 minus Var(remainder) divided by Var(seasonal plus remainder)), banded weak below 0.30, moderate below 0.60, strong at 0.60 or above. Each season gets an index, its percentage above or below the trend level, reported in calendar order, with the peak and trough named.

Use it when you have a dated history of one metric and want to know whether a seasonal cycle is real, how big it is, and which seasons peak and dip: before setting targets, staffing, or stock levels around a calendar.

Not for projecting future values (use the forecast tool), for series shorter than two full cycles, or for irregular event-driven spikes that do not repeat on a calendar.

What data do you need?

A dated history of one metric. For example, monthly revenue:

Minimum 10 rows · Best with 2-6 years of monthly or weekly history, or 3+ months of daily history

What's in the report?

Standard-library analysis: find out whether a metric has a real seasonal pattern and how big it is. Maps a date column and a numeric column, infers the data's cadence (daily, weekly, monthly) from the date spacing, and runs an STL decomposition to split the series into trend, seasonal, and noise. Reports a 0-to-1 seasonal strength score with a plain-language band, names the peak and trough seasons in calendar terms with their percentage above and below trend, gives the trend direction over the window, and states honestly how much of the variation is just noise.