Improving Forecasts of Machine Learning Algorithms by the Maximum Entropy Approach
Montag, 18. November 2019
Machine learning algorithms for time series forecasting have become increasingly powerful in recent decades. Nevertheless, not only the quality of the forecasts is important, but also their acceptance by the staff. Especially with regard to automatic forecasts, distrust may arise among dispatchers. Furthermore, long-standing employees often have a detailed overview of customer behavior, market situation and other important factors. Therefore, it makes sense to include this expert knowledge in the predictions of complex algorithms. This can be achieved through the maximum entropy approach, which is discussed in this presentation. The approach is derived in detail and applied to real data.