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We are working with 3 years of 15minute time-interval media data (1million+ entries) and have 14 external regressors (daypart, weekdays, holiday, genres). Objective is to forecast for next 15-minute intervals for given values of external regressors. What we did: - Used auto.arima (with xreg) from forecast package on this data. Result attached enter image description here

  • Created a sub-set of 5% of xreg data to check forecasting accuracy. When we compare actual media GRP values with forecasted values, there is high deviation of ~100% for certain data points.

    1. Is auto.arima the right approach for such large data?
    2. How can we improve accuracy of this model?
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It might be better to model your using a or a model, rather than with dummies. Then again, this might run into performance issues, given your large dataset.

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Other than that, I think we simply don't have enough information to be overly helpful.

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  • $\begingroup$ Thanks Stephan for replying. However, we cannot remove these dummies, since the objective is to predict for given values of daypart, day, genre combinations. Eg: objective is to be able to predict TRP for a Comedy program (genre) aired on Tuesday (weekday) at Night (daypart).. Need your help how can I improve forecast here? also should I use Neural Networks? $\endgroup$ – Radhika Gupta Mar 21 '18 at 14:16
  • $\begingroup$ A BATS or TBATS model will allow you to forecast for a given future time point, depending on the day of week and time of day that is. I suggest you take a look at that. Of course you can also use NNs. $\endgroup$ – Stephan Kolassa Mar 21 '18 at 14:22

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