The first edition
Our starting point. It learned the broad shape of busy versus quiet days, but on a fair test it barely edged out simply repeating last week.
It reads millions of live wait times to forecast how busy a park will be on any day, weeks ahead. Then it grades itself, in the open, against what actually happened.
Every figure below is forecast accuracy six weeks out, measured only on real days the model was never shown. We hold Almanac up against the two simplest ways to guess a crowd. It is ahead at every park.
| Park | Almanac | Last-week guess | Seasonal average |
|---|---|---|---|
Thorpe Park United Kingdom | 92.7 | 88.9 | 82.9 |
Alton Towers United Kingdom | 91.9 | 85.0 | 83.4 |
Legoland Windsor United Kingdom | 88.0 | 78.2 | 81.7 |
Paultons Park United Kingdom | 86.4 | 76.5 | 78.9 |
Blackpool Pleasure Beach United Kingdom | 86.6 | 79.8 | 83.5 |
Chessington United Kingdom | 81.8 | 76.0 | 79.3 |
Every park, averaged all 80+ parks | 86.4 | 83.5 | 83.4 |
Accuracy = 100 minus the average miss on a 0 to 100 busyness scale, over a six-week forecast. Higher is better. Held-out test, not days the model trained on.
Almanac ships in versions, and each one earns its place the same way: it only goes live if a fair test proves it beats the version before.
Our starting point. It learned the broad shape of busy versus quiet days, but on a fair test it barely edged out simply repeating last week.
The leap that earned the 1.0 name. Almanac learned to tell a calm midweek apart from a packed half-term, and to lean on the most reliable signal for each individual park, pulling clearly ahead of the simple methods.
Added UK school-term and holiday dates, so a quiet term-time weekday is no longer mistaken for a busy holiday crowd, and tuned Almanac to lean harder on each park’s most reliable signal. The most accurate Almanac yet.
A good forecast is more than what last year did. Almanac weighs everything that actually moves the queues.
Almanac learns from real wait times collected across more than 80 parks, updated continuously, not a one-off snapshot from last season.
Weekends, school terms, public holidays and the time of year all change how busy a park gets. Almanac knows the difference between a sleepy Tuesday and the first day of half-term.
A washout keeps crowds at home; a warm, dry spell brings them out. Almanac nudges its near-term forecasts up or down with the forecast.
When a day ends, Almanac checks its forecast against what actually happened. Each park’s real history keeps tuning the next prediction.
It is easy for a model to look clever on data it has already seen, so we never test Almanac that way. Every version is scored only on future days it was hidden from, the same test a real visitor's trip would be.
Once a day is over, Almanac compares its forecast to what really happened across more than 80 parks. The gaps it still gets wrong become the next thing we fix, which is how each edition moves the number.