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Airport Taxi Demand Forecast ML problem

I just wanted to demonstrate how typical Time-series techniques and algorithms won't fair well on all seemingly time-series forecasting problems. Time Series Extrpolation Techniques like Auto-Regression (ARIMA and cousins) works ONLY

  • if the domain knowledge is NOT influencing the traits of the series for instance the trend
  • if all time steps/lags can be framed as equally time-spaced data points without lot of gaps

In short if you have to do some predictions on a time-series like data without any domain knowledge one can go with Auto-Regression techniques.

For instance consider this use case, we wanted to forecast the airport taxi demand for the coming xmas eve and the xmas day ((of course, this means predicting the non-transit passengers load first and then the taxi share % i.e % of peope who will use taxi as their mode of transport :-)). How would you frame this as a simple time-series , even if you did will it work ? If we took all the previous xmas days and frame it as a time step, it will be too less training data to do anything meaningful. If we took the last 365 days data (xmas to xmas) and treated each day as timesteps it would be a noisy data. Passenger load on past Xmas day and the days around it would be predictive of the future Xmas days.So how to frame this as a data science problem ? Can we frame this as classic regression problem ? Let's see.

I worked with a large south east asian country's airport group, unfortunately I can share only certain information but I will drive my point with same synthentic data.


Airport Taxi Demand Forecast ML problem






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