# Forecasting Steel Production in the World--Assessments Based on Shallow and Deep Neural Networks
## 1 Introduction  
Globalization increasingly demonstrates the importance of predicting different phenomena that may occur and have huge economic and social implications. As a result, companies are increasingly more interested in technological solutions, including forecasting algorithms, which allow them to anticipate scenarios and support decisions. There is a relationship between energy intensity and production costs in the steel industry [1]. The impact of investments on the energy intensity of the steel industry is extended over time. The steel industry is a strategic sector for industrialized economies. Because it is demanding in terms of capital and energy, companies have consistently emphasized technological advances in the production process to increase productivity [2]. The steel industry is a key industrial sector in the modern world. It consists of the economical agents that perform the processes for obtaining steel-based products [3, 4]. Those products must have characteristics of safety and quality according to the standards defined for the consumer market [5]. Good decision making helps the companies produce high-quality products, innovate, fulfill customers' needs, and grow. As the companies grow, the countries' economies also grow [6], and more social needs are satisfied. As a result, market players and countries have been concerned with long-term policies that lead to sustainability in the long run. Governments have proposed solutions that help companies that follow the best practices, including activities such as recyclingand energy management, in a perspective of a circular economy. That is increasingly more important, and contrasts with obsolete managing practices of massive exploitation of natural resources [7; 8; 9]. Hence, in order to implement the best managing practices, the best management tools are required. That includes tools which provide information about the past, present, and expected future values of key indicators and variables. Statistical and machine learning forecasting models have been applied with success to fulfill the need to anticipate future scenarios, based on data of the present and past [10; 11; 12]. The present work uses a public dataset of steel production in the world. The data are used to create and train different predictive models and forecast steel production in the coming years. The models evaluated are machine learning methods based on shallow and deep neural networks. Global world data were chosen because nowadays the world is more and more integrated and the world steel production gives a strong indication of how the economy evolves [13]. The data necessary for the analysis are publicly available. The objectives and contributions of this paper are as follows:  
* to identify the models' behavior and performance, comparing the different methods with a number of different parameters;
* to forecast steel production in the world in the short and long term using neural networks;
* to evaluate the prediction performance of recurrent neural networks and convolutional neural networks;
* to evaluate the robustness of the preceding approaches to forecast nonstationary time series; and
* to contribute to validate the robustness of the model by using other variables correlated with the steel production'main variables. To analyze the use of forecasting methods, a review of bibliographic databases was performed by using the research databases Web of Science, Scopus, and Google Scholar. The keywords used were \"steel production\", \"time series\", and \"neural networks\". Figure 1 shows the result after searching the topic under study, which shows that the databases returned four articles in total. They focus on the following issues: hybrid static-sensory data modeling for prediction tasks in the basic oxygen furnace process [14]; temperature prediction for a reheating furnace by the closed recursive unit approach [15]; detecting and locating patterns in time series using machine learning [16]; and deep learning for blast furnaces [17].