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Journal articles on the topic 'Reinforcement learning. Economic forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Wee, Chee Keong, and Richi Nayak. "Adaptive load forecasting using reinforcement learning with database technology." Journal of Information and Telecommunication 3, no. 3 (2019): 381–99. http://dx.doi.org/10.1080/24751839.2019.1596470.

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2

Gros, Sebastien, and Mario Zanon. "Data-Driven Economic NMPC Using Reinforcement Learning." IEEE Transactions on Automatic Control 65, no. 2 (2020): 636–48. http://dx.doi.org/10.1109/tac.2019.2913768.

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3

Jasmin, E. A., T. P. Imthias Ahamed, and V. P. Jagathy Raj. "Reinforcement Learning approaches to Economic Dispatch problem." International Journal of Electrical Power & Energy Systems 33, no. 4 (2011): 836–45. http://dx.doi.org/10.1016/j.ijepes.2010.12.008.

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4

Liao, Yun. "Machine Learning in Macro-Economic Series Forecasting." International Journal of Economics and Finance 9, no. 12 (2017): 71. http://dx.doi.org/10.5539/ijef.v9n12p71.

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In this paper I conducted a simple experiment to using Artificial Neural Network in time-series forecasting, by combining First order Markov Switching Model and K-means algorithms, the forecasting performance of machine learning has outperformed the benchmark of time-series inflation rate forecasting. The paper reveal the potential of ANN forecasting, also provide future direction of research.

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5

Makala, Daniel, and Zongmin Li. "ECONOMIC FORECASTING WITH DEEP LEARNING: CRUDE OIL." MATTER: International Journal of Science and Technology 5, no. 2 (2019): 213–28. http://dx.doi.org/10.20319/mijst.2019.52.213228.

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6

Paruchuri, Harish. "Conceptualization of Machine Learning in Economic Forecasting." Asian Business Review 11, no. 2 (2021): 51–58. http://dx.doi.org/10.18034/abr.v11i2.532.

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Economic forecasting is a very important aspect that policymakers in the financial and corporate organization rely on because helps them to determine future events that might infringe some hardship on the economy and the citizens at large. However, the principal statistical pointers that are available to the public domain provide numerous reservations and doubts for their economics estimates as it is later released with frequent issues to major revisions and also it shows a great lag in decision making for an incoming event. To this effect, the expansion of the latest forecasting patterns was important to address the gaps. Hence, this paper examines the conceptualization of Machine learning in economic forecasting. To achieve this, the Italian economy was used as the dataset, and machine learning controlled tools were used as the method of analysis. The result obtained from this study shows that machine learning is a better model to use in economic forecasting for quick and reliable data to avert future events.

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Jeong, Jaeik, and Hongseok Kim. "DeepComp: Deep reinforcement learning based renewable energy error compensable forecasting." Applied Energy 294 (July 2021): 116970. http://dx.doi.org/10.1016/j.apenergy.2021.116970.

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Park, Rae-Jun, Kyung-Bin Song, and Bo-Sung Kwon. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning." Energies 13, no. 10 (2020): 2640. http://dx.doi.org/10.3390/en13102640.

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Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

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Rhinehart, Nicholas, and Kris M. Kitani. "First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 2 (2020): 304–17. http://dx.doi.org/10.1109/tpami.2018.2873794.

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10

Liu, Tao, Zehan Tan, Chengliang Xu, Huanxin Chen, and Zhengfei Li. "Study on deep reinforcement learning techniques for building energy consumption forecasting." Energy and Buildings 208 (February 2020): 109675. http://dx.doi.org/10.1016/j.enbuild.2019.109675.

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11

Ji, Xin, Haifeng Zhang, Jianfang Li, Xiaolong Zhao, Shouchao Li, and Rundong Chen. "Multivariate time series prediction of high dimensional data based on deep reinforcement learning." E3S Web of Conferences 256 (2021): 02038. http://dx.doi.org/10.1051/e3sconf/202125602038.

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In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.

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12

Hirata, Takaomi, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, and Kunikazu Kobayashi. "Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning." Journal of Robotics, Networking and Artificial Life 4, no. 4 (2018): 260. http://dx.doi.org/10.2991/jrnal.2018.4.4.1.

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13

Al Hajj Hassan, Lama, Hani S. Mahmassani, and Ying Chen. "Reinforcement learning framework for freight demand forecasting to support operational planning decisions." Transportation Research Part E: Logistics and Transportation Review 137 (May 2020): 101926. http://dx.doi.org/10.1016/j.tre.2020.101926.

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Hirata, Takaomi, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, and Kunikazu Kobayashi. "Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning." Proceedings of International Conference on Artificial Life and Robotics 22 (January 19, 2017): 658–61. http://dx.doi.org/10.5954/icarob.2017.os12-3.

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15

Li, Yuming, Pin Ni, and Victor Chang. "Application of deep reinforcement learning in stock trading strategies and stock forecasting." Computing 102, no. 6 (2019): 1305–22. http://dx.doi.org/10.1007/s00607-019-00773-w.

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16

Hügelschäfer, Sabine, and Anja Achtziger. "Reinforcement, Rationality, and Intentions: How Robust Is Automatic Reinforcement Learning in Economic Decision Making?" Journal of Behavioral Decision Making 30, no. 4 (2017): 913–32. http://dx.doi.org/10.1002/bdm.2008.

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17

Liu, Weirong, Peng Zhuang, Hao Liang, Jun Peng, and Zhiwu Huang. "Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning." IEEE Transactions on Neural Networks and Learning Systems 29, no. 6 (2018): 2192–203. http://dx.doi.org/10.1109/tnnls.2018.2801880.

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18

Lee, Cheng-Ming, and Chia-Nan Ko. "Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network." Energies 9, no. 12 (2016): 987. http://dx.doi.org/10.3390/en9120987.

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19

Zengeler, Nico, and Uwe Handmann. "Contracts for Difference: A Reinforcement Learning Approach." Journal of Risk and Financial Management 13, no. 4 (2020): 78. http://dx.doi.org/10.3390/jrfm13040078.

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We present a deep reinforcement learning framework for an automatic trading of contracts for difference (CfD) on indices at a high frequency. Our contribution proves that reinforcement learning agents with recurrent long short-term memory (LSTM) networks can learn from recent market history and outperform the market. Usually, these approaches depend on a low latency. In a real-world example, we show that an increased model size may compensate for a higher latency. As the noisy nature of economic trends complicates predictions, especially in speculative assets, our approach does not predict courses but instead uses a reinforcement learning agent to learn an overall lucrative trading policy. Therefore, we simulate a virtual market environment, based on historical trading data. Our environment provides a partially observable Markov decision process (POMDP) to reinforcement learners and allows the training of various strategies.

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Liu, Hui, Chengqing Yu, Haiping Wu, Zhu Duan, and Guangxi Yan. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting." Energy 202 (July 2020): 117794. http://dx.doi.org/10.1016/j.energy.2020.117794.

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21

Li, Tian, Yongqian Li, and Baogang Li. "Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8192368.

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Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN) based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.

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22

Han, Chuanjia, Bo Yang, Tao Bao, Tao Yu, and Xiaoshun Zhang. "Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer." Energies 10, no. 5 (2017): 638. http://dx.doi.org/10.3390/en10050638.

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23

Fu, Yang, Xiaoyan Guo, Yang Mi, et al. "The distributed economic dispatch of smart grid based on deep reinforcement learning." IET Generation, Transmission & Distribution 15, no. 18 (2021): 2645–58. http://dx.doi.org/10.1049/gtd2.12206.

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24

Mosavi, Amirhosein, Yaser Faghan, Pedram Ghamisi, et al. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics." Mathematics 8, no. 10 (2020): 1640. http://dx.doi.org/10.3390/math8101640.

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The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

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25

Liu, Hui, Chengming Yu, Chengqing Yu, Chao Chen, and Haiping Wu. "A novel axle temperature forecasting method based on decomposition, reinforcement learning optimization and neural network." Advanced Engineering Informatics 44 (April 2020): 101089. http://dx.doi.org/10.1016/j.aei.2020.101089.

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26

Wu, Qiong, Xu Chen, Zhi Zhou, Liang Chen, and Junshan Zhang. "Deep Reinforcement Learning With Spatio-Temporal Traffic Forecasting for Data-Driven Base Station Sleep Control." IEEE/ACM Transactions on Networking 29, no. 2 (2021): 935–48. http://dx.doi.org/10.1109/tnet.2021.3053771.

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27

Zhang, Wenyu, Qian Chen, Jianyong Yan, Shuai Zhang, and Jiyuan Xu. "A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting." Energy 236 (December 2021): 121492. http://dx.doi.org/10.1016/j.energy.2021.121492.

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28

Zizzo, Daniel John. "Implicit learning of (boundedly) rational behaviour." Behavioral and Brain Sciences 23, no. 5 (2000): 700–701. http://dx.doi.org/10.1017/s0140525x00613432.

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Stanovich & West's target article undervalues the power of implicit learning (particularly reinforcement learning). Implicit learning may allow the learning of more rational responses–and sometimes even generalisation of knowledge–in contexts where explicit, abstract knowledge proves only of limited value, such as for economic decision-making. Four other comments are made.

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29

Dai, Pengcheng, Wenwu Yu, Guanghui Wen, and Simone Baldi. "Distributed Reinforcement Learning Algorithm for Dynamic Economic Dispatch With Unknown Generation Cost Functions." IEEE Transactions on Industrial Informatics 16, no. 4 (2020): 2258–67. http://dx.doi.org/10.1109/tii.2019.2933443.

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30

Wang, Jiao, Xueping Li, and Xiaoyan Zhu. "Intelligent dynamic control of stochastic economic lot scheduling by agent-based reinforcement learning." International Journal of Production Research 50, no. 16 (2012): 4381–95. http://dx.doi.org/10.1080/00207543.2011.592158.

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31

Zhou, Suyang, Zijian Hu, Wei Gu, et al. "Combined heat and power system intelligent economic dispatch: A deep reinforcement learning approach." International Journal of Electrical Power & Energy Systems 120 (September 2020): 106016. http://dx.doi.org/10.1016/j.ijepes.2020.106016.

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32

Wang, Yu, Hualei Zou, Xin Chen, Fanghua Zhang, and Jie Chen. "Adaptive Solar Power Forecasting based on Machine Learning Methods." Applied Sciences 8, no. 11 (2018): 2224. http://dx.doi.org/10.3390/app8112224.

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Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

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33

Sivalingam, Kumar Chandar, Sumathi Mahendran, and Sivanandam Natarajan. "Forecasting Gold Prices Based on Extreme Learning Machine." International Journal of Computers Communications & Control 11, no. 3 (2016): 372. http://dx.doi.org/10.15837/ijccc.2016.3.2009.

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<p>In recent years, the investors pay major attention to invest in gold market ecause of huge profits in the future. Gold is the only commodity which maintains ts value even in the economic and financial crisis. Also, the gold prices are closely elated with other commodities. The future gold price prediction becomes the warning ystem for the investors due to unforeseen risk in the market. Hence, an accurate gold rice forecasting is required to foresee the business trends. This paper concentrates on orecasting the future gold prices from four commodities like historical data’s of gold rices, silver prices, Crude oil prices, Standard and Poor’s 500 stock index (S&amp;P500) ndex and foreign exchange rate. The period used for the study is from 1st January 000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered eed forward neural networks called Extreme Learning Machine (ELM) is used which as good learning ability. Also, this study compares the five models namely Feed orward networks without feedback, Feed forward back propagation networks, Radial asis function, ELMAN networks and ELM learning model. The results prove that he ELM learning performs better than the other methods.</p>

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34

Claveria, Oscar. "Forecasting with Business and Consumer Survey Data." Forecasting 3, no. 1 (2021): 113–34. http://dx.doi.org/10.3390/forecast3010008.

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In a context of growing uncertainty caused by the COVID-19 pandemic, the opinion of businesses and consumers about the expected development of the main variables that affect their activity becomes essential for economic forecasting. In this paper, we review the research carried out in this field, placing special emphasis on the recent lines of work focused on the exploitation of the predictive content of economic tendency surveys. The study concludes with an evaluation of the forecasting performance of quarterly unemployment expectations for the euro area, which are obtained by means of machine learning methods. The analysis reveals the potential of new analytical techniques for the analysis of business and consumer surveys for economic forecasting.

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35

Kurihara, Yutaka, and Akio Fukushima. "AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries." Applied Economics and Finance 6, no. 3 (2019): 1. http://dx.doi.org/10.11114/aef.v6i3.4126.

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This paper examines the validity of forecasting economic variables by using machine learning. AI (artificial intelligence) has been improved and entering our society rapidly, and the economic forecast is no exception. In the real business world, AI has been used for economic forecasts, but not so many studies focus on machine learning. Machine learning is focused in this paper and a traditional statistical model, the autoregressive (AR) model is also used for comparison. A comparison of using an AR model and machine learning (LSTM) to forecast GDP and consumer price is conducted using recent cases from G7 countries. The empirical results show that the traditional forecasting AR model is a little more appropriate than the machine learning model, however, there is little difference to forecast consumer price between them.

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36

Brabenec, Tomàš, and Petr Šuleř. "Machine learning forecasting of CR and PRC balance of trade." SHS Web of Conferences 73 (2020): 01004. http://dx.doi.org/10.1051/shsconf/20207301004.

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International trade is an important factor of economic growth. While foreign trade has existed throughout the history, its political, economic and social importance has grown significantly in the last centuries. The objective of the contribution is to use machine learning forecasting for predicting the balance of trade of the Czech Republic (CR) and the People´s Republic of China (PRC) through analysing and machine learning forecasting of the CR import from the PRC and the CR export to the PRC. The data set includes monthly trade balance intervals from January 2000 to June 2019. The contribution investigates and subsequently smooths two time series: the CR import from the PRC; the CR export to the PRC. The balance of trade of both countries in the entire monitored period is negative from the perspective of the CR. A total of 10,000 neural networks are generated. 5 neural structures with the best characteristics are retained. Neural networks are able to capture both the trend of the entire time series and its seasonal fluctuations, but it is necessary to work with time series lag. The CR import from the PRC is growing and it is expected to grow in the future. The CR export to the PRC is growing and it is expected to grow in the future, but its increase in absolute values will be slower than the increase of the CR import from the PRC.

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37

Rutkauskas, Aleksandras Vytautas, and Tomas Ramanauskas. "BUILDING AN ARTIFICIAL STOCK MARKET POPULATED BY REINFORCEMENT‐LEARNING AGENTS." Journal of Business Economics and Management 10, no. 4 (2009): 329–41. http://dx.doi.org/10.3846/1611-1699.2009.10.329-341.

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In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement-learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision-making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup. Along with that a parallel version of the model is presented, which is mainly based on research of current changes in the market, as well as on search of newly emerged consistent patterns, and which has been repeatedly used for optimal decisions’ search experiments in various capital markets.

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38

Cicceri, Giovanni, Giuseppe Inserra, and Michele Limosani. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study." Mathematics 8, no. 2 (2020): 241. http://dx.doi.org/10.3390/math8020241.

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In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model.

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Zhang, Aiqi, Meiyi Sun, Jiaqi Wang, Zhiyi Li, Yanbo Cheng, and Cheng Wang. "Deep Reinforcement Learning-Based Multi-Hop State-Aware Routing Strategy for Wireless Sensor Networks." Applied Sciences 11, no. 10 (2021): 4436. http://dx.doi.org/10.3390/app11104436.

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With the development of wireless sensor network technology, the routing strategy has important significance in the Internet of Things. An efficient routing strategy is one of the fundamental technologies to ensure the correct and fast transmission of wireless sensor networks. In this paper, we study how to combine deep learning technology with routing technology to propose an efficient routing strategy to cope with network topology changes. First, we use the recurrent neural network combined with the deep deterministic policy gradient method to predict the network traffic distribution. Second, the multi-hop node state is considered as the input of a double deep Q network. Therefore, the nodes can make routing decisions according to the current state of the network. Multi-hop state-aware routing strategy based on traffic flow forecasting (MHSA-TFF) is proposed. Simulation results show that the MHSA-TFF can improve transmission delay, average routing length, and energy efficiency.

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40

Adimabua Ojugo, Arnold, and Elohor Ekurume. "Predictive Intelligent Decision Support Model in Forecasting of the Diabetes Pandemic Using a Reinforcement Deep Learning Approach." International Journal of Education and Management Engineering 11, no. 2 (2021): 40–48. http://dx.doi.org/10.5815/ijeme.2021.02.05.

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41

Zhu, Juncheng, Zhile Yang, Monjur Mourshed, et al. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches." Energies 12, no. 14 (2019): 2692. http://dx.doi.org/10.3390/en12142692.

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Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

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42

Lin, Lin, Xin Guan, Yu Peng, Ning Wang, Sabita Maharjan, and Tomoaki Ohtsuki. "Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy." IEEE Internet of Things Journal 7, no. 7 (2020): 6288–301. http://dx.doi.org/10.1109/jiot.2020.2966232.

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43

SEN, Safa, and Sara Almeida de Figueiredo. "Forecasting Bank Failure with Machine Learning Models: A study on Turkish Banks." Journal of Economics, Finance and Accounting Studies 3, no. 2 (2021): 51–59. http://dx.doi.org/10.32996/jefas.2021.3.2.6.

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Forecasting bank failures has been an essential study in the literature due to their significant impact on the economic prosperity of a country. Acting as an intermediary player, banks channel funds from those with surplus capital to those who require capital to carry out their economic activities. Therefore, it is essential to generate early warning systems that could warn banks and stakeholders in case of financial turbulence. In this paper, three machine learning models named as GLMBoost, XGBoost, and SMO were used to forecast bank failures. We used commercial bank failure data of Turkey between 1997 and 2001, where we have 17 failed and 20 healthy banks. Our results show that the Sequential Minimal Optimization and GLMBoost provide the same performance when classifying failed banks, while GLMBoost performs better in AUC and SMO when considering total classification success. Lastly, XGBoost, one of the most recent and robust classification models, surprisingly underperformed in all three metrics we used in research.

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44

Ting, Wang, and Li Xueyong. "Research on short-term electric load forecasting based on extreme learning machine." E3S Web of Conferences 53 (2018): 02009. http://dx.doi.org/10.1051/e3sconf/20185302009.

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As an important support for the development of the national economy, the power industry plays a role in ensuring economic operations. Time series prediction can process dynamic data, is widely used in economics and engineering, and especially is of great practical value in using historical data to predict future development. Under the guidance of extreme learning machine and time series theory, this paper applies the extreme learning machine to the study of time series, and builds a model for load forecasting research. Load forecasting plays an important role in power planning, affecting planning operation modes, power exchange schemes, etc., so load forecasting is very necessary in power planning. First, establish an extreme learning machine model; second, the short-term load forecasting is performed by different activation functions to verify the performance of the activation function.1 After empirical analysis, the activation function with the best predictive ability is obtained.

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45

Huang, Shian-Chang, and Cheng-Feng Wu. "Energy Commodity Price Forecasting with Deep Multiple Kernel Learning." Energies 11, no. 11 (2018): 3029. http://dx.doi.org/10.3390/en11113029.

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Oil is an important energy commodity. The difficulties of forecasting oil prices stem from the nonlinearity and non-stationarity of their dynamics. However, the oil prices are closely correlated with global financial markets and economic conditions, which provides us with sufficient information to predict them. Traditional models are linear and parametric, and are not very effective in predicting oil prices. To address these problems, this study developed a new strategy. Deep (or hierarchical) multiple kernel learning (DMKL) was used to predict the oil price time series. Traditional methods from statistics and machine learning usually involve shallow models; however, they are unable to fully represent complex, compositional, and hierarchical data features. This explains why traditional methods fail to track oil price dynamics. This study aimed to solve this problem by combining deep learning and multiple kernel machines using information from oil, gold, and currency markets. DMKL is good at exploiting multiple information sources. It can effectively identify the relevant information and simultaneously select an apposite data representation. The kernels of DMKL were embedded in a directed acyclic graph (DAG), which is a deep model and efficient at representing complex and compositional data features. This provided a solid foundation for extracting the key features of oil price dynamics. By using real data for empirical testing, our new system robustly outperformed traditional models and significantly reduced the forecasting errors.

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Livieris, Ioannis E. "Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks." Algorithms 12, no. 4 (2019): 85. http://dx.doi.org/10.3390/a12040085.

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During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning.

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Zhou, Wusheng. "Prediction of Urban and Rural Tourism Economic Forecast Based on Machine Learning." Scientific Programming 2021 (September 22, 2021): 1–7. http://dx.doi.org/10.1155/2021/4072499.

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With the rapid development of tourism, tourism revenue, as one of the important indicators to measure the development of the tourism economy, has high research value. The quasi-prediction of tourism revenue can drive the development of a series of related industries and accelerate the development of the domestic economy. When forecasting tourism income, it is necessary to examine the causal relationship between tourism income and local economic development. The traditional cointegration analysis method is to extract the promotion characteristics of tourism income to the local economy and construct a tourism income prediction model, but it cannot accurately describe the causal relationship between tourism income and local economic development and cannot accurately predict tourism income. We propose an optimized forecasting method of tourism revenue based on time series. This method first conducts a cointegration test on the time series data of the relationship between tourism income and local economic development, constructs a two-variable autoregressive model of tourism income and local economy, and uses the swarm intelligence method to test the causal relationship and the relationship between tourism income and local economic development, calculate the proportion of tourism industry, define the calculation result as the direct influence factor of tourism industry on the local economy, calculate the relevant effect of local tourism development and economic income, and construct tourism income optimization forecast model. The simulation results show that the model used can accurately predict tourism revenue.

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Apatova, Nataliya Vladimirovna, and Vitaliy Borisovich Popov. "FORECASTING BANKRUPTCY OF ENTERPRISES USING ARTIFICIAL INTELLIGENCE." Scientific Bulletin: finance, banking, investment., no. 2 (51) (2020): 113–20. http://dx.doi.org/10.37279/2312-5330-2020-2-113-120.

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With increasing competition, the market situation is constantly changing and many enterprises are at risk of bankruptcy. There are various methods for predicting the insolvency of manufacturing enterprises, but artificial intelligence methods allow this to be more accurately. Global data used for the analysis and forecasting of bankruptcy reveal the general patterns of this economic phenomenon. An analysis of publications on predicting bankruptcy of enterprises made it possible to identify frequently used mathematical models constructed for foreign firms and giving high accuracy for Russian ones. However, a comparative analysis of various methods led to the conclusion that they need to update due to economic conditions external to the company, as well as the increased computing power of modern computers. The authors selected artificial intelligence methods that allow you to build a trained neural network and make it universal for predicting the bankruptcy of any production enterprise. The authors constructed an algorithm and a neural network, and made a bankruptcy forecast was carried out with an accuracy of 89 %. It substantiates the construction and use of a mathematical model with a high ability to predict the bankruptcy of various enterprises in any region of the world based on the latest neural network technologies of deep learning (Deep learning). Some of the deep learning technologies are the Keras and TensorFlow libraries — these are APIs (application programming interface) designed for specialists in the analysis and modeling of subject areas. The article presents the algorithm of the neural network, the results of its testing.

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Lannelongue, K., M. De Milly, R. Marcucci, S. Selevarangame, A. Supizet, and A. Grincourt. "Compositional Grounded Language for Agent Communication in Reinforcement Learning Environment." Journal of Autonomous Intelligence 2, no. 3 (2019): 1. http://dx.doi.org/10.32629/jai.v2i3.56.

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In a context of constant evolution of technologies for scientific, economic and social purposes, Artificial Intelligence (AI) and Internet of Things (IoT) have seen significant progress over the past few years. As much as Human-Machine interactions are needed and tasks automation is undeniable, it is important that electronic devices (computers, cars, sensors…) could also communicate with humans just as well as they communicate together. The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines, illustrated with chat-bots. Nonetheless, using this technology is not sufficient, as they often give inappropriate or unrelated answers, usually when the subject changes. To improve this technology, the problem of defining a communication language constructed from scratch is addressed, in the intention to give machines the possibility to create a new and adapted exchange channel between them. Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment, the convergence toward a common ‘’language’’ is analyzed, exactly as it is supposed to have happened for humans in the past. By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality, rapidly converging evolution of syntactic communication is obtained, opening the way of a meaningful language between machines.

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Uddin, Minhaz, Shraboni Rudra, and Mohammed Nazim Uddin. "Forecasting the Long Term Economics Status of Bangladesh Using Machine Learning Approaches from 2016-2036." International Journal of Computer Communication and Informatics 1, no. 1 (2019): 58–64. http://dx.doi.org/10.34256/ijcci19110.

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It is a piece of happy news for us that Bangladesh has been now converted to a developing country. The United Nation and World Bank have recognized it. But they have a condition that we need to continue this economic progress till 2024 for getting a permanent recognition. The economic condition depends on many factors like Gross Domestic Product (GDP), Personal Saving, Private Sector Investment, Gross National Income (GNI) per capita, Human Assets Index (HAI) and Economic Vulnerability Index (EVI). This paper portrays the forecast of the long-term economic condition of Bangladesh as an independent variable which is a year and the dependent variables are GDP, private sector investment and personal saving. The living conditions of a country depend on GDP. Personal saving and Private Sector Investment are also important parts of a country’s economy. If we will forecast these attributes properly, then we can determine the economic condition of Bangladesh and living status of the people more accurately. Therefore, we can determine that Bangladesh can fulfil the condition of getting permanent recognized as a developing country. For forecasting these attributes, we proposed a model which consists of Karl Pearson’s coefficient and modified linear regression techniques. For improving performance, we modify linear regression by gradient boosting. This experiment shows that our model gives us more accurate forecasting about GDP, Private sector investment and Personal Saving.

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