Day Prior Year (DoPY)

YAML type: dopy | Category: Periodic

Compares the current day to the same calendar day one year earlier.

Description

DoPY provides year-over-year daily context. It needs roughly one year of history to be meaningful.

How it works

Day Prior Year (DoPY) illustration

Day Prior Year compares today to the same calendar day one year earlier and applies the same percentual-change and mode rules as other period detectors.

It needs roughly a year of history to be meaningful. Holiday misalignment between years is common -- enable special-date suppression when calendar effects dominate.

Setup

Require sufficient history in the data view. Tune percentualChange for YoY volatility of the metric.

Configure detectors on the data view Analysis tab or in YAML. Validate with Data Preview before enabling production schedules.

Settings

Parameter	Default	Description
`percentualChange`	`50`	Minimum absolute % change to flag
`mode`	`both`	Direction -- see Allowed values

Allowed values

`mode`

Value	Meaning
`above`	Flag increases / values above the comparison (or high side of a band)
`below`	Flag decreases / values below the comparison (or low side of a band)
`both`	Flag either direction

Shared series filters

Most detectors also accept optional series filters in params:

Parameter	Purpose
`maxGapAmount`	Skip series when gap amount is too high (`null` = no limit). Catalog create-default is often `5`.
`minValue`	Skip series whose average value is below this (`null` = no limit). Catalog create-default is often `1000`.
`maxValue`	Skip series whose average value is above this (optional; omit/`null` = no limit)
`useSpecialDates`	When `true`, suppress anomalies that overlap special dates

When a filter skips a series, Data Preview shows the reason.

Configuration example

analysis:
  detectors:
    - type: dopy
      params:
        percentualChange: 50.0
        mode: both

Using the detector

Interpret with calendar knowledge (holidays shifting weekdays). Use special dates when YoY holiday misalignment is expected.

Use cases

Retail same-day YoY sales
Traffic or demand vs last year
Detecting structural growth/decline day by day

Specifics

Week/month YoY: wpy, mpy.