Data-driven forecasting and its role in enhanced decision-making

Decision-making is a crucial process for any organization, since it involves the selection of the most effective action from a variety of options. In this context, data plays an important role in driving decisions. Analyzing data allows us to extract patterns that enable better decision-making for achieving specific goals. However, to make the right decisions to control the behavior of a system, it is necessary to take into account different factors, which can be challenging. Indeed, in dynamic systems, numerous variables change over time, and understanding the future state of these systems can be crucial for controlling the system. Predicting future states based on historical data is known as time series forecasting, which can be divided into univariate and multivariate forecasting, with the latter being particularly relevant due to its consideration of multiple variables. Deep Learning methods enhance decision-making by identifying patterns in complex datasets. As data complexity grows, techniques like Automated Machine Learning optimize model performance. The present study introduces a novel methodology that integrates multivariate time series forecasting into decision-making frameworks. We used Automated-Machine Learning to develop a predictive model for forecasting future system states, aiding optimal decision-making. The study compares machine learning models based on performance metrics and computational cost across various domains, including weather monitoring, power consumption, hospital electricity monitoring, and exchange rates. We also analyzed the importance of the hyperparameters in identifying key factors affecting model performance. The obtained results show that Neural Architecture Search method can improve state predictor design by reducing computational resources and enhancing performance.