Attendance Forecast

Panorama's Attendance Forecast helps educators identify students who may be at risk of future absenteeism earlier than traditional attendance metrics alone. It combines a student’s current absence rate with research-backed attendance pattern signals to estimate likely attendance risk for the rest of the school year.

Related articles:

• The Power of Absence Streaks in Attendance Forecasting

What is Attendance Forecast

Attendance Forecast is a research-backed attendance risk indicator in Student Success. It is designed to help educators move from retrospective attendance monitoring to earlier, more proactive intervention.

Instead of looking only at how many days a student has missed so far, the feature also considers how those absences are distributed. Panorama research found that absence patterns provide meaningful predictive value beyond total absences alone.

Attendance Forecast presents this information as simple, educator-friendly designations:

• Clear — lower projected attendance risk

• Watch — moderate projected attendance risk

• Warning — elevated projected attendance risk

Why Attendance Forecast matters

Traditional attendance metrics are useful for reporting outcomes, but they often surface risk too late. Districts commonly rely on attendance percentage or chronic absenteeism thresholds, which can identify a problem only after a pattern is already well established.

Attendance Forecast was built to address two practical problems for educators:

• Absence totals alone do not tell the full story. Two students can have similar year-to-date attendance rates while facing very different future attendance trajectories.

• Many existing indicators arrive too late for early intervention. By the time a student is clearly chronically absent on a traditional metric, important outreach opportunities may already have been missed.

Panorama’s research across more than 11 million student-year observations found that attendance patterns, especially absence streak behavior, improve prediction of future absenteeism compared with absence rate alone.

How Attendance Forecast works

Attendance Forecast estimates a student’s projected full-year absence rate using a fixed linear model. It then translates that estimate into a simple category that educators can use in day-to-day workflows.

The current model uses four inputs derived from the student’s attendance record:

• Current absence rate — how many days the student has missed so far

• Number of absence streaks — how many separate absence events the student has had

• Longest absence streak — the longest continuous run of absences

• Percent of year complete — how far along the school year is

Research behind Attendance Forecast

Holding total absences constant, students with more absence streaks are more likely to miss more school.

Students with a longer single continuous streak tend to have lower future absence rates than students whose absences are spread across more separate streaks, when comparing similar totals.

Adding streak features to absence rate improved forecasting performance and outperformed approaches based on day-of-week absence patterns.

Simple example

Consider two students early in the year:

• Student A: 5 absences in 1 continuous streak

• Student B: 3 absences across 3 separate streaks

Even though Student A has more total absences, Panorama’s research found that students with patterns like Student B had a higher average future absence rate than students with patterns like Student A.

Patterns of attendance matter, not just totals.

Forecast categories at launch

The UI displays the forecast as a rounded integer percent to avoid overstating precision.

A streak is one or more consecutive full-day absences on instructional weekdays. A single missed day counts as a streak. Non-instructional days are ignored, so a Friday absence followed by a Monday absence counts as one continuous 2-day streak if no school occurred in between.

Where it appears

Attendance Forecast appears within existing attendance experiences so educators can find the information quickly from their familar tools and workflows.

It appears in these places:

Attendance Overview

Attendance Forecast helps educators quickly scan for students with elevated projected attendance risk. Teams can isolate students in Watch or Warning to focus intervention planning.

Daily Attendance Dashboard

Attendance Forecast on the Daily Attendance Dashboard aggregates the number of students with either Watch or Warning designations.

Student profile

In the student profile, the forecast appears alongside current attendance context so users can interpret risk in the full student picture.

How educators should use the Attendance Forecast

1. Review a student’s current attendance information alongside the forecast category.

2. Use Clear, Watch, and Warning to prioritize which students may need outreach first.

3. Sort or filter student lists by forecast category to identify high-risk groups quickly.

4. Open the student profile for additional attendance context before taking action.

5. Use the forecast as an early signal to inform intervention planning, not as the only decision input.

What else to know

Attendance Forecast is not an AI feature. It uses a research-backed linear model with fixed inputs and coefficients, which helps make the behavior more explainable.

It also does not account for prior year attendance trends. The feature is year scoped, so each student has a new forecast for each school year.

Frequently asked questions

What exactly is being predicted?

Attendance Forecast displays a student’s projected full-year absence rate, not just the expected remainder-of-year rate.

Do excused absences, tardies, or partial-day absences count?

No. The Attendance Forecast uses only full-day absences. Partial absences and excused versus unexcused status are not included in the streak calculation.

Why can a student with fewer absences appear riskier?

Because absence patterns matter, a student with fewer total absences spread across many separate streaks may be more likely to continue missing school than a student with more absences concentrated in one continuous event.

Is this an AI feature?

No. Attendance Forecast uses a research-backed linear model with fixed inputs and coefficients.

When is the feature most useful during the year?

The signal is expected to be most valuable in the earlier and middle portions of the school year, especially once enough attendance history exists to establish a pattern. The strongest practical value is roughly between days 20 and 100 of the school year.

Does Attendance Forecast consider prior year attendance trends?

No. Attendance Forecast does not account for prior year attendance trends.

What happens for students with insufficient attendance history?

Attendance Forecast follows the same visibility rules as the current attendance indicator for students with limited attendance history. If the existing attendance experience limits what is shown for insufficient data, Attendance Forecast will do the same.

Does Attendance Forecast work everywhere?

Attendance Forecast is available on the new students overview experience. It will not work in Strategic Priorities Advisor.