/time-series-forecasting-airline-passenger-traffic

Build models for forecasting Airline passenger traffic by utilizing several algorithms for time series analysis.

Primary LanguageJupyter Notebook

Time Series Forecastig for Airline Passenger Traffic Dataset

Problem Statement

An airline company has the data of the number of passengers that have travelled with them on a particular route for the past few years. Using this data, they want to see if they can forecast the number of passengers for the next twelve months.

Making this forecast could be quite beneficial to the company as it would help them take some crucial decisions like -

  • What capacity aircraft should they use?
  • When should they fly?
  • How many air hostesses and pilots do they need?
  • How much food should they stock in their inventory?
  1. Quantity: Number of passengers
  2. Granularity: Flights from city A to city B; i.e., flights for a particular route
  3. Frequency: Monthly
  4. Horizon: 1 year (12 months)

Dataset

The dataset is uploaded along with an external variable dataset, which is used at the end of the analysis.