Demand Forecasting
See Every Room Night Before It Books
ML-powered occupancy and revenue forecasts with confidence bands. Pickup, pace, cancellations, seasonality, and local events combine into a single forecast that refreshes nightly.
6-Month Occupancy Forecast
30-day
81.2%
±3.1%
60-day
86.7%
±5.4%
90-day
88.1%
±7.2%
5–9%
MAPE at 7 days
365d
Forecast horizon
6+
Signals modelled
Nightly
Retraining
Model Explainer
Six Signals, One Forecast
Every stay-date prediction combines the factors revenue managers have always used — now weighted automatically with explainable feature attribution.
Pickup
Rate of new bookings per day by stay date, segmented by lead time buckets and channel source.
Pace
On-the-books versus same-time-last-year and same-time-last-period. Positive or negative pace drives the forecast adjustment.
Cancellations
Historical cancellation curves by segment, rate plan, lead time, and channel. Predicts net bookings rather than gross.
Seasonality
Day-of-week, month, and multi-year seasonal patterns captured with Fourier components to avoid overfitting.
Events
Local events, school holidays, conferences, and public holidays. Auto-ingested for 4,000+ cities or imported from CSV.
Macro signals
Comp-set rate movement, destination search trends, and flight arrival data where available — layered as exogenous variables.
Dashboard Previews
Built for Revenue Managers, Readable by Owners
A month-grid heatmap shows occupancy at a glance. A pace view compares on-the-books against same-time-last-year. Every chart drills into stay-level detail so you can move from signal to action in one click.
- Heatmap calendar for 30, 60, and 90-day views
- Pace curves by segment, channel, and room type
- Revenue projection with P10/P50/P90 bands
- Exportable CSV, PDF, and PNG per chart
- Drill-through to stay-level forecast contributors
ML vs Rule-Based
Why the Model Matters
Rule-based forecasting still has a place — for explainability and small markets. Here’s where an ML model changes the game.
| Aspect | Rule-based | SwiftGuest ML |
|---|---|---|
| Setup time | Weeks of manual rule tuning | Hours — trains on 12 months of PMS data automatically |
| Adapts to shocks | No — rules break in new conditions | Yes — retrains nightly on most recent pace data |
| Confidence bands | Single point estimate only | P10/P50/P90 bands per forecast horizon |
| Event handling | Manual override per event | Auto-detected and weighted from historical event response |
| Explainability | Fully explainable by definition | Feature attribution per prediction — see which factors drove the number |
| Accuracy at 7 days out | Typical MAPE 14–22% | Typical MAPE 5–9% on SwiftGuest data |
MAPE = Mean Absolute Percentage Error. Lower is better.
Data Inputs
Powered by Your Data, Enriched With Context
No CSVs to maintain. SwiftGuest already knows your reservations, pace, and cancellations — we add the external context so the model sees the full picture.
Reservations history
12+ months from PMS
On-the-books pace
Real-time from PMS
Cancellation curves
Per rate plan and channel
Event calendar
4,000+ cities auto-ingested
Weather & holidays
Destination-specific
Comp-set rates
Connected OTA shopper
What You Can Do
From Forecast to Decision
Rate strategy
Feed forecasts into the rate engine so rules and ML pricing know the demand curve they’re pricing against.
Overbooking model
Predict cancellation rates per segment and set safe overbooking levels that lift occupancy without walk risk.
Staffing and ops
Export day-level occupancy forecasts to housekeeping and F&B so labour plans match real demand.
Owner reporting
30/60/90-day revenue projections with confidence bands, exportable as PDF or pushed to ownership dashboards.
How It Works
Four Steps, Fully Automated
Ingest
Reservations, pace, cancellations, and rate shops stream into the model continuously.
Train
Gradient-boosted regression and exponential smoothing retrain nightly on your full history.
Predict
Daily forecasts for the next 365 days with P10/P50/P90 confidence bands and feature attribution.
Act
Forecasts feed rate rules, overbooking models, staffing plans, and owner reports.
Frequently asked questions
How much historical data do I need?
A minimum of six months of reservation history gives usable forecasts; twelve months captures a full seasonal cycle. Newer properties can start with a blended model that borrows patterns from similar destinations until they have their own history.
How often does the model retrain?
Retraining runs nightly on the latest bookings, cancellations, and rate shops. Forecasts regenerate for every stay date in the 365-day horizon, so your dashboard is fresh every morning.
What accuracy can I expect?
On SwiftGuest customer data, median MAPE (mean absolute percentage error) is 5–9% at 7 days out, 9–14% at 30 days out, and 14–20% at 90 days out. Every forecast ships with P10/P50/P90 confidence bands so you can see the range of likely outcomes.
Can I see why the model predicted a number?
Yes. Every forecast point has feature attribution: how much of the prediction came from pickup pace, seasonality, events, cancellations, and rate position. Click a day on the chart to see the breakdown.
How are events detected?
SwiftGuest auto-ingests events from public calendars, ticketing APIs, and conference listings for major destinations. You can also upload a CSV, tag custom dates, or mark past events so the model learns your property’s response curve.
Does it handle multiple room types and segments?
Yes. Forecasts are generated at the room-type and segment level (corporate, leisure, group, OTA, direct) then rolled up to total property. You can view every level in the dashboard and export at any granularity.
What happens when market conditions change?
Because the model retrains nightly on recent pace, it adjusts within days of a shift. You can also set guardrails that pause ML-driven pricing decisions when forecast uncertainty exceeds a threshold, reverting to manual control.
Related features
Forecast the next 365 days in your first week
Connect your PMS, let the model train overnight, and wake up to a dashboard that tells you exactly where demand is heading.