# Assuming 'data' is a pandas Series with a datetime index result = seasonal_decompose(data, model='multiplicative')
The is used when the seasonal variation is relatively constant throughout the series: $$Y = \textTrend + \textSeasonality + \textNoise$$
To see a pattern, you need history. Ideally, collect at least of data. 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.
In the world of data analysis, the trend is your friend, but # Assuming 'data' is a pandas Series with
❌ – It can evolve (e.g., holiday shopping starting earlier). ❌ Ignoring calendar effects – Easter, Ramadan, leap years, trading days. ❌ Applying multiplicative model when data has zeros or negatives – Use additive. ❌ Not testing for stationarity – SARIMA requires differencing. ❌ Overfitting – Using too many seasonal periods (e.g., daily + weekly + yearly without enough history).
Yt=Tt+St+Itcap Y sub t equals cap T sub t plus cap S sub t plus cap I sub t 2. The Multiplicative Model This means December sales are typically 25% higher
Take all the ratios calculated in Step 2 for a specific month (e.g., all Januaries from the past 5 years) and find the average (mean) or median. This removes the random "noise," leaving you with a pure seasonal index.
This guide explores the concept of seasonality, how to distinguish it from noise, and the step-by-step methods used to calculate it.
This result represents the seasonal factor combined with the irregular noise.