Prediction
Our API provides a predictive travel demand intelligence. We provide predictions for 1 year into the future.
The API has the following settings: product dimension and time resolution.
The product dimension defines the type of date for which the predictions or historical data are requested. It can be either search date or travel date, or lead time.
The time resolution defines the granularity of the historical data as well as the predictions, such as daily, weekly or monthly.
Strategic insights
Our predictions are provided with the following strategic insights:
ZYTLYN Demand: ZYTLYN Demand represents the search demand, expressed in calibrated passenger numbers for a specific route.
ZYTLYN Realised Demand Passengers: ZYTLYN Realised Demand Passengers represents the booking passengers demand, expressed in calibrated passenger numbers for a specific route.
year over 2019 growth: Represents the growth of the ZYTLYN Demand for a specific route compared to 2019 (in percentage).
year over year growth: Represents the growth of the ZYTLYN Demand for a specific route compared to the previous year.
These are the related forecast uncertainties:
ZYTLYN Demand: Uncertainty estimates associated with ZYTLYN demand forecasts for a given origin-destination pair and the prediction time point (e.g. week/month).
ZYTLYN Realised Demand Passengers: Uncertainty estimates associated with ZYTLYN Realised demand Passengers forecasts for a given origin-destination pair and the prediction time point (e.g. week/month).
Additional insights are provided with our history:
Demand to Realisation ratio: This ratio is the output of ZYTLYN Demand divided by ZYTLYN Realised Demand.
Realised Demand length of stay: This field provides the distribution of ZYTLYN Realised Demand across predefined length of stay (LOS) buckets, expressed as percentages. Each bucket represents a range of days, where the left bound is inclusive and the right bound is exclusive (i.e., [a–b) means stays ≥ a days and < b days).
Realised Demand percentage one way: This is the one-way demand, expressed as a percentage, based on ZYTLYN Realised Demand.
Realised Demand percentage round trip: This is the round-trip demand, expressed as a percentage, based on ZYTLYN Realised Demand.
Demand Percentage one way: This is the one-way demand, expressed as a percentage, based on ZYTLYN Demand.
Demand Percentage round trip: This is the round-trip demand, expressed as a percentage, based on ZYTLYN Demand.
ZYTLYN Realised Demand: This represents air travel booking demand.
ZYTLYN Realised Demand Passengers: This represents air travel booking demand sum of passengers.
Passengers per Booking ratio: This ratio is the output of ZYTLYN Realised Demand Passenger divided by ZYTLYN Realised Demand.
Cabin Class distribution: This is the breakdown of ZYTLYN Realised Demand by cabin class, expressed as a percentage for each category.
Realised Demand multi city: This represents air travel multi-city type booking demand.
Passengers per Booking ratio multi city: This ratio is the output of total number of multi city passengers divided by ZYTLYN Realised Demand Multi City.
Lead time predictions are provided for the following variable:
ZYTLYN Demand: This contains the lead time predictions, expressed as a float number for each lead time bucket category, referred to ZYTLYN Demand.
Realised Demand Length of Stay
This field provides the distribution of ZYTLYN Realised Demand across predefined length of stay (LOS) buckets, expressed as percentages. Each bucket represents a range of days, where the left bound is inclusive and the right bound is exclusive (i.e., [a–b) means stays ≥ a days and < b days).
Example
Bucket Definitions
"0-1": stays ≥ 0 and < 1 day
"1-2": stays ≥ 1 and < 2 days
"2-3": stays ≥ 2 and < 3 days
"3-4": stays ≥ 3 and < 4 days
"4-6": stays ≥ 4 and < 6 days
"6-10": stays ≥ 6 and < 10 days
"10-20": stays ≥ 10 and < 20 days
"20-30": stays ≥ 20 and < 30 days
"30+": stays ≥ 30 days
These percentages represent the share of realised demand for each length of stay category and should sum to approximately 100%.
Lead Time Predictions
Lead time predictions are provided for the following variable:
ZYTLYN Demand: This contains the lead time predictions, expressed as a float number for each lead time bucket category, referred to ZYTLYN Demand. Each value corresponds to the proportion of ZYTLYN Demand for searches made within a specific lead time range. Each bucket represents a range of days, where the left bound is inclusive and the right bound is exclusive (i.e.,
[a–b)means searches ≥ a days and < b days before travel week)
Example
Bucket Definitions
"0-1": search ≥ 0 and < 1 day before the travel week.
"1-2": search ≥ 1 and < 2 days before the travel week.
"2-3": search ≥ 2 and < 3 days before the travel week.
"3-4": search ≥ 3 and < 4 days before the travel week.
"4-5": search ≥ 4 and < 6 days before the travel week.
"5-6": search ≥ 6 and < 10 days before the travel week.
These ratios represent the share of ZYTLYN Demand for each lead time bucket and should sum to approximately 1.
Product dimension
Our product is available along two dimensions: search date and travel date.
Search date corresponds to the period when the user searches for a flight.
Travel date corresponds to the period when the user takes the flight.
Lead Time corresponds to the period of time between the search/booking date and travel date.
Time resolution
The time resolution defines the granularity of the predictions and historical data, such as weekly or monthly.
weekly: The weekly time resolution will provide predictions and historical data aggregated by week.
monthly: The monthly time resolution will provide predictions and historical data aggregated by month.
Historical data range
The historical data range depends on your current subscription.
The historical data corresponds to real values that have been recorded in the past.
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