Calculating Seasonality

If the average ratio for December is 1.25, your December Seasonal Index is 125. This means December sales are typically 25% higher than the average month.

Accurately evaluating these cycles allows data scientists, marketers, and financial analysts to optimize inventory levels, scale ad spend, and generate accurate forecasts. Mathematical Fundamentals of Seasonality Time series data ( Ytcap Y sub t ) typically breaks down into three core components: : The long-term directional movement. Seasonal ( Stcap S sub t ) : Recurring patterns within a fixed calendar window. Irregular ( Itcap I sub t ) : Random noise or unexpected anomalies.

For a time series ( Y_t = T_t \times S_t \times R_t ): calculating seasonality

Used when seasonal variations remain constant in absolute magnitude regardless of the overall trend level.

Divide the actual observation ($Y$) by the Moving Average ($MA$). If the average ratio for December is 1

Used when the amplitude of seasonal fluctuations scales proportionally with the baseline trend. This framework is common in business, marketing, and web traffic data.

Centered Moving Average (e.g., a 12-month average) to smooth out short-term fluctuations and isolate the trend. Step 2: Detrending Divide the actual data by the moving average trend for each period (Multiplicative Model). Step 3: Average Residuals Average these resulting "ratios" for each specific month across all years to get the final seasonal coefficients. Wikipedia +2 3. Built-in Excel Functions 10 sites Seasonality - Wikipedia Detection. A seasonality plot of US electricity usage A radar chart using polar coordinates: Though there is large variability in ... Wikipedia 14.4 Seasonal Indices – Introduction to Statistics – Second Edition Calculating Seasonal Indices. The seasonal index for a particular season is found by dividing the average value for that season by... eCampusOntario Pressbooks Mastering FP&A Seasonality Index Calculator in Excel - Sparkco Oct 12, 2025 — Mathematical Fundamentals of Seasonality Time series data (

from statsmodels.tsa.seasonal import seasonal_decompose import pandas as pd