Seasonality Inputs

This article explains Seasonality inputs in the Timing modal.

You will learn:

  • What seasonality controls.

  • How to enter seasonal patterns.

  • How seasonality interacts with frequency and delays.

Seasonality lets you skew accruals towards some periods and away from others while keeping annual or total values anchored.

1

What seasonality does

Seasonality defines relative weights for periods within a cycle, for example:

  • Higher revenue in December and lower in January.

  • Higher volumes in summer months.

  • Lower demand in holiday weeks.

The timing engine uses these weights to reallocate accruals across periods without changing the total over the cycle.

2

Entering seasonality patterns

You can configure seasonality by:

  • Entering multipliers for each period of a cycle (for example 12 values for monthly seasonality across a year).

  • Using a preset pattern where available.

  • Importing a seasonal index series from external data.

Values are typically relative, such as 0.8, 1.0, 1.2, representing below average, average and above average periods.

3

How seasonality interacts with base frequency

The base frequency and schedule determine when accruals can happen. Seasonality then adjusts how much weight each eligible period receives, for example:

  • A yearly total is allocated more heavily to some months than others.

  • A weekly schedule has some weeks scaled up or down based on the seasonal pattern.

Seasonality does not change total volume over the defined seasonal cycle unless you explicitly scale it.

4

Seasonality and cash timing

Seasonality affects accrual timing. Cash timing is still governed by delays and payment terms:

  • Seasonal peaks in accrual will create corresponding peaks in receivables or payables when delays are present.

  • Cashflow peaks will lag accrual peaks by the specified delays.

Check both P&L and cash outputs when adjusting seasonality.

5

Validating seasonal settings

To validate seasonality inputs:

  • Use the Timing preview to see the shaped pattern over time.

  • Compare seasonal results to historical data where available.

  • Ensure the pattern makes sense for the business (for example matching known busy seasons).

Adjust carefully to avoid overfitting or unrealistic profiles.


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