Score private: 15.41 and public: 16.37 • Did not work on local validation • Model with 4 variables (On a Call Time, Total Handle Time, Total Calls, and Agent Headcount) • One reason for not improving the score considering additional variables, can be that, additional variables are calculated based on Agent Headcount of that hour. Also reach us at 20. 2019 DevScope. Score private: 15.41 and public: 16.37 • Did not work on local validation • Model with 4 variables (On a Call Time, Total Handle Time, Total Calls, and Agent Headcount) • One reason for not improving the score considering additional variables, can be that, additional variables are calculated based on Agent Headcount of that hour.

In this post I will go over the technical aspects of automatic forecast generation, as well as some of the organizational considerations that will arise when deciding to go with an automatic forecast generating system. As I said earlier, in many fields, including my field of retail demand forecasting, most commercial forecasting packages do perform automatic forecast generation. Several open source packages do so as well, most notably Rob Hyndman's auto.arima() Both the commercial products and the open source packages that I mentioned work based on the idea of using information criteria to choose the best forecasting model: You fit a bunch of models, and then select the model with the lowest AIC, BIC, AICc, etc….(typically this is done in lieu of out of sample validation -- see this presentation for details). There is however a major caveat: all of these methods work within a single family of models.