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Datathron

6
votes

4 thoughts on “Datathron

  1. 4
    votes

    I really like that the whole process of modelling is backed up by sound business logics. I find that your approach of incorporating exogenous features such as data on relevant stocks and Google trends adds a significant extent of originality in the adopted research methodology.
    Data prep is conducted in compliance with the core theoretical requirements.
    The implementation of rolling sample is correct.
    What I would like to advise on the model is to consider statistical significance, especially for the estimates associated with the exogenous explanatory variables. Also, looking at the plot of actual vs predicted 1-step-ahead data, I might state that the model captures really tightly the series volatility for the first 12 000 observations. In order to tune better the model you might consider the reason behind the deteriorated performance aftermath. Once again, my suggestion is to inspect how statistical significance of delivered estimates changes over time.
    Congrets on reporting the figure of directional symmetry!
    Also, I really like the way your workflow is organized taking advantage of both R and Python utilizing the one that is best suited to the research task at hand.
    Great job, guys!

  2. -1
    votes

    It seems you did some work, but it is not readable – no color highlights on the code, no plots, etc. Please update your article, there is functionality on the website, so you can upload directly .ipynb files. Or even if you prefer you can upload it as html.
    As a jury I am not able to give you any good score.
    After you update it, the mentors will be able to give you feedback and recommend you some more approaches.

    1. 6
      votes

      We would appreciate it if for the next Datathon this site also supports R notebooks (Rmd), it will be convenient for people using predominantly R, not only Python (like what we have done above).

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