Automated Machine Learning (AutoML) is an area of research that focuses on developing methods to generate machine learning models automatically. The idea of being able to build machine learning models with very little human intervention represents a great opportunity for the practice of applied machine learning. However, there is very little information on how to design an AutoML system in practice. Most of the research focuses on the problems facing optimization algorithms and leaves out the details of how that would be done in practice. In this paper, we propose a frame of reference for building general AutoML systems. Through a narrative review of the main approaches in the area, our main idea is to distill the fundamental concepts in order to support them in a single design. Finally, we discuss some open problems related to the application of AutoML for future research.
Forecasting for inventory control is the process of calculating the inventory needs to fulfill future consumer demand. In general, this process is divided into two sub-processes. The first sub-process receives the current inventory information and forecasts future information, e.g. forecasts future demand from the demand information in the past. The second sub-process uses the forecast information as input to make inventory decisions, e.g. use a product demand forecast to decide how many units of this product to buy. Recent works highlight the importance of integrating forecasting with final inventory decisions, however, there is very little empirical evidence to support that integrating the decision is the best solution. In this work, we propose to explore the effect of integrating the inventory decision into the forecasting problem and compare it with the state-of-the-art approaches. For this, we evaluated the approaches in different operational tasks belonging to our business. Our preliminary findings show that predicting operative decisions instead of demand information could be better and the benefit can be capitalized even in low data scenarios.
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