Predicting Sales for Rossmann Drug Stores
نویسندگان
چکیده
In this paper we examined four different methods for time series forecasting: Random Forests, Gradient Boosting, Hidden Markov Models, and Recurrent Neural Networks. We found that using Gradient Boosting yielded the best results with root-mean-square percent error (RMPSE) of 10.439% (785 of 3429).
منابع مشابه
Rossmann Store Sales
The objective of this project is to forecast sales in euros at 1115 stores owned by Rossmann, a European pharmaceutical company. The input to our algorithm is a feature vector (discussed in section 3) of a single day of data for that store. We tried using a number of algorithms, but mainly gradient boosting, to output the predicted total sales in euros for the given store that day. Rossmann pro...
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