{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T12:54:37Z","timestamp":1791550477468,"version":"4.3.4"},"reference-count":83,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T00:00:00Z","timestamp":1596067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on \u201cElsevier\u201d, \u201cSpringer\u201d, and \u201cIEEE Xplore\u201d that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.<\/jats:p>","DOI":"10.3390\/a13080186","type":"journal-article","created":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T04:15:31Z","timestamp":1596168931000},"page":"186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Review on Recent Advancements in FOREX Currency Prediction"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1051-0499","authenticated-orcid":false,"given":"Md. Saiful","family":"Islam","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Port City International University, Chittagong 4202, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6422-1895","authenticated-orcid":false,"given":"Emam","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2688-927X","authenticated-orcid":false,"given":"Abdur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Port City International University, Chittagong 4202, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Shahadat","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0244-3561","authenticated-orcid":false,"given":"Karl","family":"Andersson","sequence":"additional","affiliation":[{"name":"Pervasive and Mobile Computing Laboratory, Lule\u00e5 University of Technology, S-931 87 Skellefte\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,30]]},"reference":[{"key":"ref_1","unstructured":"Levinson, M. (2014). The Economist Guide to Financial Markets: Why They Exist and How They Work, The Economist."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.asoc.2016.01.048","article-title":"Heuristic based trading system on Forex data using technical indicator rules","volume":"43","author":"Ozturk","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12001","DOI":"10.1016\/j.eswa.2009.03.057","article-title":"Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach","volume":"36","author":"Anastasakis","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1093\/rfs\/12.1.61","article-title":"FX spreads and dealer competition across the 24-h trading day","volume":"12","author":"Huang","year":"1999","journal-title":"Rev. Financ. Stud."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1080\/14697688.2013.773458","article-title":"Time zone normalization of FX seasonality","volume":"13","author":"Masry","year":"2013","journal-title":"Quant. Financ."},{"key":"ref_6","first-page":"4","article-title":"Evaluation of the profitability of technical analysis for Asian currencies in the forex spot market for short-term trading","volume":"5","author":"Adariani","year":"2012","journal-title":"AU-GSB e-JOURNAL"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.future.2018.01.027","article-title":"Pricing estimation of a barrier option in an IoT scenario","volume":"110","author":"Cuomo","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.future.2017.07.062","article-title":"A computational method for the European option price in an Internet of Things framework","volume":"107","author":"Cuomo","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0261-5606(98)00011-4","article-title":"The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence","volume":"17","author":"Lui","year":"1998","journal-title":"J. Int. Money Financ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0165-1889(02)00185-9","article-title":"Speculative markets and the effectiveness of price limits","volume":"28","author":"Westerhoff","year":"2003","journal-title":"J. Econ. Dyn. Control."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.dss.2010.07.008","article-title":"Profitability of technical analysis in financial and commodity futures markets\u2014A reality check","volume":"50","author":"Yen","year":"2010","journal-title":"Decis. Support Syst."},{"key":"ref_12","unstructured":"Li, J., and Tsang, E.P. (1999, January 1\u20135). Improving Technical Analysis Predictions: An Application of Genetic Programming. Proceedings of the Twelfth International FLAIRS Conference, Orlando, FL, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1111\/0022-1082.00163","article-title":"Data-snooping, technical trading rule performance, and the bootstrap","volume":"54","author":"Sullivan","year":"1999","journal-title":"J. Financ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S1386-4181(01)00032-5","article-title":"Price clustering in foreign exchange spot markets","volume":"5","author":"Sopranzetti","year":"2002","journal-title":"J. Financ. Mark."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1016\/S0305-0548(03)00064-9","article-title":"Regression neural network for error correction in foreign exchange forecasting and trading","volume":"31","author":"Chen","year":"2004","journal-title":"Comput. Oper. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1016\/j.cor.2004.06.024","article-title":"A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates","volume":"32","author":"Yu","year":"2005","journal-title":"Comput. Oper. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yu, L., Wang, S., and Lai, K.K. (2005, January 22\u201325). Adaptive smoothing neural networks in foreign exchange rate forecasting. Proceedings of the International Conference on Computational Science, Atlanta, GA, USA.","DOI":"10.1007\/11428862_72"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/j.dss.2005.09.001","article-title":"A hybrid model for exchange rate prediction","volume":"42","author":"Ince","year":"2006","journal-title":"Decis. Support Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Adamopoulos, A., Andreou, A., Georgopoulos, E., Ioannou, N., and Likothanassis, S. (1998). Currency forecasting using recurrent RBF networks optimized by genetic algorithms. Decision Technologies for Computational Finance, Springer.","DOI":"10.1007\/978-1-4615-5625-1_38"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/S1057-5219(99)00006-X","article-title":"Forecasting currency prices using a genetically evolved neural network architecture","volume":"8","year":"1999","journal-title":"Int. Rev. Financ. Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1023\/A:1020989601082","article-title":"Exchange-rates forecasting: A hybrid algorithm based on genetically optimized adaptive neural networks","volume":"20","author":"Andreou","year":"2002","journal-title":"Comput. Econ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1002\/for.838","article-title":"Forecasting daily foreign exchange rates using genetically optimized neural networks","volume":"21","author":"Nag","year":"2002","journal-title":"J. Forecast."},{"key":"ref_24","unstructured":"Abraham, A., and Chowdhury, M.U. (November, January 29). An intelligent forex monitoring system. Proceedings of the 2001 International Conferences on Info-Tech and Info-Net, Proceedings (Cat. No. 01EX479), Beijing, China."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/S0165-0114(98)00286-3","article-title":"Fuzzy ARIMA model for forecasting the foreign exchange market","volume":"118","author":"Tseng","year":"2001","journal-title":"Fuzzy Sets Syst."},{"key":"ref_26","unstructured":"Abraham, A. (2002, January 12\u201317). Analysis of hybrid soft and hard computing techniques for forex monitoring systems. Proceedings of the 2002 IEEE World Congress on Computational Intelligence, 2002 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE\u201902, Proceedings (Cat. No. 02CH37291), Honolulu, HI, USA."},{"key":"ref_27","unstructured":"Neagoe, V., Iatan, R., and Iatan, H. (July, January 28). A nonlinear neuro-fuzzy model for prediction of daily exchange rates. Proceedings of the World Automation Congress, Seville, Spain."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/0378-4266(90)90009-Q","article-title":"Statistical study of foreign exchange rates, empirical evidence of a price change scaling law, and intraday analysis","volume":"14","author":"Dacorogna","year":"1990","journal-title":"J. Bank. Financ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/07350015.1996.10524628","article-title":"High-frequency data and volatility in foreign-exchange rates","volume":"14","author":"Zhou","year":"1996","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1057\/palgrave.jibs.8490245","article-title":"Foreign exchange risk management practices and products used by Australian firms","volume":"24","author":"Batten","year":"1993","journal-title":"J. Int. Bus. Stud."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/S0378-4371(02)01881-2","article-title":"Analysis of high-resolution foreign exchange data of USD-JPY for 13 years","volume":"324","author":"Mizuno","year":"2003","journal-title":"Physica A"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0261-5606(01)00043-2","article-title":"Real trading patterns and prices in spot foreign exchange markets","volume":"21","author":"Payne","year":"2002","journal-title":"J. Int. Money Financ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7653","DOI":"10.1016\/j.eswa.2014.06.009","article-title":"Text mining for market prediction: A systematic review","volume":"41","author":"Nassirtoussi","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"41","DOI":"10.14257\/ijgdc.2017.10.6.04","article-title":"Weighted fuzzy rule based sentiment prediction analysis on tweets","volume":"10","author":"Basha","year":"2017","journal-title":"Int. J. Grid Distrib. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Eng, M.H., Li, Y., Wang, Q.G., and Lee, T.H. (2008, January 19\u201321). Forecast forex with ANN using fundamental data. Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering, Taipei, Taiwan.","DOI":"10.1109\/ICIII.2008.302"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.ieri.2014.09.083","article-title":"Foreign currency exchange rates prediction using cgp and recurrent neural network","volume":"10","author":"Rehman","year":"2014","journal-title":"IERI Procedia"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kamruzzaman, J., and Sarker, R.A. (2003, January 14\u201317). Forecasting of currency exchange rates using ANN: A case study. Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, Nanjing, China.","DOI":"10.1109\/ICNNSP.2003.1279395"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Balabanov, T., Zankinski, I., and Dobrinkova, N. (2011, January 6\u201310). Time series prediction by artificial neural networks and differential evolution in distributed environment. Proceedings of the International Conference on Large-Scale Scientific Computing, Sozopol, Bulgaria.","DOI":"10.1007\/978-3-642-29843-1_22"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2015.12.028","article-title":"A Forex trading expert system based on a new approach to the rule-base evidential reasoning","volume":"51","author":"Dymova","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Putra, A.R.P., Permanasari, A.E., and Fauziati, S. (2016, January 5\u20136). I forex trend prediction technique using multiple indicators and multiple pairs correlations DSS: A software design. Proceedings of the 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia.","DOI":"10.1109\/ICITEED.2016.7863248"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Achchab, S., Bencharef, O., and Ouaarab, A. (2017, January 11\u201313). A combination of regression techniques and cuckoo search algorithm for FOREX speculation. Proceedings of the World Conference on Information Systems and Technologies, Madeira, Portugal.","DOI":"10.1007\/978-3-319-56535-4_23"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.eswa.2014.08.004","article-title":"Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment","volume":"42","author":"Nassirtoussi","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s10844-018-0504-9","article-title":"Word sense disambiguation application in sentiment analysis of news headlines: An applied approach to FOREX market prediction","volume":"52","author":"Seifollahi","year":"2019","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ranjit, S., Shrestha, S., Subedi, S., and Shakya, S. (2018, January 25\u201327). Comparison of algorithms in Foreign Exchange Rate Prediction. Proceedings of the 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), Kathmandu, Nepal.","DOI":"10.1109\/CCCS.2018.8586826"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.procs.2019.01.189","article-title":"Forecasting of forex time series data based on deep learning","volume":"147","author":"Ni","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ma\u0144dziuk, J., and Rajkiewicz, P. (2016, January 24\u201329). Neuro-evolutionary system for FOREX trading. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada.","DOI":"10.1109\/CEC.2016.7744384"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.eswa.2019.01.012","article-title":"Literature review: Machine learning techniques applied to financial market prediction","volume":"124","author":"Henrique","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Raimundo, M.S., and Okamoto, J. (2018, January 23\u201325). SVR-wavelet adaptive model for forecasting financial time series. Proceedings of the 2018 International Conference on Information and Computer Technologies (ICICT), DeKalb, IL, USA.","DOI":"10.1109\/INFOCT.2018.8356851"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"16777","DOI":"10.1109\/ACCESS.2019.2895022","article-title":"Bayesian compressed vector autoregression for financial time-series analysis and forecasting","volume":"7","author":"Taveeapiradeecharoen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s40595-018-0108-x","article-title":"Analyzing predictive performance of linear models on high-frequency currency exchange rates","volume":"5","author":"Serjam","year":"2018","journal-title":"Vietnam J. Comput. Sci."},{"key":"ref_51","first-page":"345","article-title":"A hybridized ELM-Jaya forecasting model for currency exchange prediction","volume":"32","author":"Das","year":"2020","journal-title":"J. King Saud-Univ.-Comput. Inf. Sci."},{"key":"ref_52","first-page":"32","article-title":"Construction of currency portfolios by means of an optimized investment strategy","volume":"5","author":"Chandrinos","year":"2018","journal-title":"Oper. Res. Perspect."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.asoc.2017.04.014","article-title":"Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network","volume":"58","author":"Pradeepkumar","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.physa.2018.09.021","article-title":"An optimized feature reduction based currency forecasting model exploring the online sequential extreme learning machine and krill herd strategies","volume":"513","author":"Das","year":"2019","journal-title":"Physica A"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Galeshchuk, S., and Mukherjee, S. (2017, January 21\u201323). FOREX Trading Strategy Optimization. Proceedings of the International Symposium on Distributed Computing and Artificial Intelligence, Porto, Portugal.","DOI":"10.1007\/978-3-319-60882-2_9"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"7071","DOI":"10.1007\/s00521-018-3552-8","article-title":"A hybridized ELM using self-adaptive multi-population-based Jaya algorithm for currency exchange prediction: An empirical assessment","volume":"31","author":"Das","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3545","DOI":"10.1007\/s00500-019-03863-1","article-title":"Sliding-window metaheuristic optimization-based forecast system for foreign exchange analysis","volume":"23","author":"Chou","year":"2019","journal-title":"Soft Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.asoc.2017.12.047","article-title":"Combining Support Vector Machine with Genetic Algorithms to optimize investments in Forex markets with high leverage","volume":"64","author":"Neves","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Thu, T.N.T., and Xuan, V.D. (2018, January 11\u201312). Using support vector machine in FoRex predicting. Proceedings of the 2018 IEEE International Conference on Innovative Research and Development (Icird), Bangkok, Thailand.","DOI":"10.1109\/ICIRD.2018.8376303"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s10115-018-1303-x","article-title":"Short-term trend prediction in financial time series data","volume":"61","author":"Toroslu","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6653","DOI":"10.1007\/s00500-016-2216-9","article-title":"A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms","volume":"21","author":"Toroslu","year":"2017","journal-title":"Soft Comput."},{"key":"ref_62","unstructured":"Dash, R. (2017). Performance analysis of an evolutionary recurrent Legendre Polynomial Neural Network in application to FOREX prediction. J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.physa.2017.05.044","article-title":"An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction","volume":"486","author":"Dash","year":"2017","journal-title":"Physica A"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.eswa.2018.03.044","article-title":"AIRMS: A risk management tool using machine learning","volume":"105","author":"Chandrinos","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.asoc.2018.02.043","article-title":"Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate prediction","volume":"67","author":"Dash","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1007\/s00521-015-2032-7","article-title":"A new NN-PSO hybrid model for forecasting Euro\/Dollar exchange rate volatility","volume":"31","author":"Hajizadeh","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Fan, M.H., Chen, M.Y., and Liao, E.C. (2019). A deep learning approach for financial market prediction: Utilization of Google trends and keywords. Granul. Comput., 1\u201310.","DOI":"10.1007\/s41066-019-00181-7"},{"key":"ref_68","unstructured":"Koskivaara, E. (2003). Artificial Neural Networks in Auditing: State of the Art, Turku Centre for Computer Science. Technical Report."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.swevo.2017.05.003","article-title":"Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms","volume":"36","author":"Ravi","year":"2017","journal-title":"Swarm Evol. Comput."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jfds.2019.01.001","article-title":"COSMOS trader\u2013Chaotic Neuro-oscillatory multiagent financial prediction and trading system","volume":"5","author":"Lee","year":"2019","journal-title":"J. Financ. Data Sci."},{"key":"ref_71","first-page":"219","article-title":"FOREX Rate Prediction: A Hybrid Approach Using Chaos Theory and Multivariate Adaptive Regression Splines","volume":"Volume 515","author":"Satapathy","year":"2017","journal-title":"Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1007\/s40815-019-00688-w","article-title":"Chaotic Interval Type-2 Fuzzy Neuro-oscillatory Network (CIT2-FNON) for Worldwide 129 Financial Products Prediction","volume":"21","author":"Lee","year":"2019","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.simpat.2018.04.008","article-title":"ENMX: An elastic network model to predict the FOREX market evolution","volume":"86","author":"Contreras","year":"2018","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1016\/j.asoc.2018.09.017","article-title":"Reinforcement learning applied to Forex trading","volume":"73","author":"Neves","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.physa.2017.03.014","article-title":"Identification of market trends with string and D2-brane maps","volume":"479","year":"2017","journal-title":"Physica A"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Taveeapiradeecharoen, P., and Aunsri, N. (2018, January 25\u201328). Dynamic model averaging for daily forex prediction: A comparative study. Proceedings of the 2018 International Conference on Digital Arts, Media and Technology (ICDAMT), Phayao, Thailand.","DOI":"10.1109\/ICDAMT.2018.8376549"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.eswa.2019.04.067","article-title":"Time series modeling and forecasting based on a Markov chain with changing transition matrices","volume":"133","author":"Wilinski","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"103736","DOI":"10.1016\/j.jedc.2019.103736","article-title":"Improving forecasts with the co-range dynamic conditional correlation model","volume":"108","author":"Fiszeder","year":"2019","journal-title":"J. Econ. Dyn. Control."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s10115-017-1052-2","article-title":"An algorithmic framework for frequent intraday pattern recognition and exploitation in forex market","volume":"53","author":"Goumatianos","year":"2017","journal-title":"Knowl. Inf. Syst."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.eswa.2017.08.011","article-title":"A stacked generalization system for automated FOREX portfolio trading","volume":"90","author":"Petropoulos","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1007\/s11704-017-6472-3","article-title":"Improved expert selection model for forex trading","volume":"12","author":"Zhu","year":"2018","journal-title":"Front. Comput. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Pushpa, P. (2017, January 3\u20135). Context Information based FOREX services. Proceedings of the EAI International Conference on Ubiquitous Communications and Network Computing, Bangalore, India.","DOI":"10.1007\/978-3-319-73423-1_8"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Weerathunga, H., and Silva, A. (2018, January 26\u201329). DRNN-ARIMA Approach to Short-term Trend Forecasting in Forex Market. Proceedings of the 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka.","DOI":"10.1109\/ICTER.2018.8615580"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/8\/186\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:52:58Z","timestamp":1760176378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/8\/186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,30]]},"references-count":83,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["a13080186"],"URL":"https:\/\/doi.org\/10.3390\/a13080186","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,30]]}}}