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Tomorrows guest predictions as a strategic source

Tomorrows guest predictions as a strategic source

Project Profile

The prediction of standardized statistical key figures in tourism is always a challenge. Especially in times of COVID! We used an approach based on mobile phone data in Vienna and an approach using existing data sources of Statistik Austria like an extensive set of additive data to train our models to achieve a 3 month forecast for arrivals and overnight stays in Vienna. In cooperation with the AIT (Austrian Institute for Technology / http://www.ait.ac.at) we succeeded in producing an assessable forecast.

At a glance - essential project data

DurationFrom 2/1/2020 to 10/28/2020 with about 9 months of full engagement
Data and ToolsMarket - Tourism
 • Mobile data Flight data VIE
 • EU-COVID Data
 • 30 years Arrivals/Nights
 • Weather
 • Google Trends
 • Global Holiday Calendar
Integration • Forecast information on client's systems
 • PowerBI as visualization of forecast data
 • Continuous process of data preparation with partner for our models
AI Methods
 • Deep Learning
 • Time Series
 • ML - Regression

Engagement Use-Case

Forecast model for arrivals and overnight stays for a city, region, ... over the period of 2 years forecast horizon.

Client motivation / Solution aims

  • Bugetation

  • To properly plan and deploy marketing efforts depending on future tourism currents

  • Achieve strategic tool for future campaigns

  • Get efficiency in tourism planning

AI Approach

AI key technology used in our solutionCollaboration with AIT as independent quality assurance. Increase granularity of input data (overnight stays) by extrapolation with daily mobile data.
Solution Approach • Short-term model: LSTM TimeSeries
 • Long-term model: Logistic Growth (Gompertz)
 • DNN Timeseries model. Decomposition of the time series into three additive components for trend seasonality and residuals.
Project ApproachSimply agile
Project TypeProject
ML Integration and ML Operations • Operations Integration API
 • FTP
 • VIsualizationPower-BI
 • Azure Synapse

Insights and Details

We use different approaches for forecasting in different stages of tourism development after lockdown

The DL model learns from the April data that a reduction is due and applies this also with COVID consideration - See left half of the picture!

Our forecast model as an interaction of many competences. From the extrapolation to the forecast!

The forecast results were presented in a small PowerBI dataset.