Prediction systems

Datathon Telenor Solution – Winner Winner-Data Dinner- The Telenor Case

Based on one month data with flight fails, we have to make time-series analysis and predict the future amount of fails and also to find out how many of the ravens sent are not going to make it. Communication being an essential part of the human existence and lives are solely dependent on regular communication,it is necessary to find out the failures,so as to improve them and make the communication system better and to predict further flaws in the system and correct them.As we dig deep into this data,we will find valuable insights that would help us to improve the failure rate and downgrade it,so as to improve the success rate and provide a better working system to work on in the near future.


2 thoughts on “Datathon Telenor Solution – Winner Winner-Data Dinner- The Telenor Case

  1. 1

    Great work, guys!
    How would you tackle seasonality in your data? I saw you were using seasonal ARIMA – any other approaches you would recommend to use?

    1. 3

      Thanks for your appreciation. We used the concept of deseasonalization or seasonal adjustment. There are ARCH and GARCH style models which we wanted to use but found this algorithm as an easy and proper approach

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