# Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks
## 2 Related Work  
Research on the predictability of economic data has a long history in economics; thus economic data mining systems have gained popularity during the last two decades. The main reason for the increasing popularity of these systems is their ability to support economic decision-making considerations such as information acquisition and decision-making error costs. A number of rewarding studies have been carried out in recent years, and some useful outcomes are briefly presented below. [PERSON] et al. [1] proposed a novel classifier based on the Artificial Immune Network (AINE) to evaluate the applicants' credit scores. Additionally, they conducted a variety of experiments, utilizing two real-world datasets of the banking industries to explore the effectiveness of their proposedmodel. The presented experimental results demonstrated that the AINE-based classifier outperformed state-of-the-art classifiers, in terms of prediction accuracy. Furthermore, the authors claimed that the proposed model can provide the credit card issuer with accurate and valuable information of credit scoring analysis to avoid making incorrect decisions. [PERSON] et al. [2] proposed a personal and intelligent decision support system that utilizes a data mining approach for the selection of bank telemarketing clients. Their primary goal was to model the success of subscribing to a long-term deposit using attributes that were known before the telemarketing call was executed. For this purpose, they utilized a large dataset of 150 features related to bank client, product, and social-economic attributes. In the modeling phase of their proposed framework, a semi-automatic feature selection was explored, which resulted in selecting a reduced set of 22 features. In the sequel, they evaluated the classification performance of various machine learning algorithms. Their experimental results revealed that ANN demonstrated the highest classification accuracy. [PERSON] et al. [3] provided an extensive review of neural networks applications on several economic and business classification problems. Their investigation revealed that most of the research was aimed at financial distress and bankruptcy problems, stock price forecasting, and decision support, with special attention given to classification tasks. Furthermore, the authors claimed that most research papers argued that neural networks outperformed conventional approaches such as discriminant analysis, Bayesian classifiers, and linear regression. [PERSON] and [PERSON] [15] proposed an ANN model incorporating a new penalty function, which gives variable penalties to the misclassification of instances considering their individual significance (profit of correct classification and/or cost of misclassification). More specifically, they modified the sum of square errors function by changing its values with respect to the profit of each instance in order to generate individual penalties. To this end, they have introduced seven versions of ANN classifiers in total where each of them consisted of a modification of the original ANN classifier. The performance of their proposed framework was evaluated on two real-world datasets from fraud detection and a dataset about bank marketing. The reported numerical experiments revealed that there was no champion model for all datasets, but the different versions of the proposed model exhibited statistical improvement in the total net profit as compared to several classification algorithms. In more recent works, [PERSON] et al. [4] introduced a novel supervised learning model, called the Naive Associative Classifier (NAC), which boosts simplicity, transparency, transportability, and accuracy. Their proposed model was evaluated using finance-related datasets including bank telemarketing, credit assignment, bankruptcy, and banknote authentication. The numerical experiments presented that NAC exhibited considerable capability in solving financial classification problems, highlighting the adequacy of the proposed model for decision support.