Demand Forecasting of individual Probability Density Functions with Machine Learning

by   F. Wick, et al.

Demand forecasting is a central component for many aspects of supply chain operations, as it provides crucial input for subsequent decision making like ordering processes. While machine learning methods can significantly improve prediction accuracy over traditional time series forecasting, the calculated predictions are often just point estimations for the conditional mean of the underlying probability distribution, and the most powerful approaches, like deep learning, are usually opaque in terms of how its individual predictions can be interpreted. Using the novel supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted instead of single numbers. While metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare and this work proposes new techniques for both qualitative and quantitative evaluation methods. Additionally, each single prediction obtained with this framework is explainable. This is a major benefit in particular for practitioners, as this allows them to avoid "black-box" models and understand the contributing factors for each individual prediction.



There are no comments yet.


page 6


Cyclic Boosting – an explainable supervised machine learning algorithm

Supervised machine learning algorithms have seen spectacular advances an...

Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting

Short-term forecasting is an important tool in understanding environment...

Probabilistic supervised learning

Predictive modelling and supervised learning are central to modern data ...

Intermittent Demand Forecasting with Deep Renewal Processes

Intermittent demand, where demand occurrences appear sporadically in tim...

Exploiting statistical dependencies of time series with hierarchical correlation reconstruction

While we are usually focused on predicting future values of time series,...

Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models

Machine learning methods have been adopted in the literature as contende...

A unified machine learning approach to time series forecasting applied to demand at emergency departments

There were 25.6 million attendances at Emergency Departments (EDs) in En...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.