[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123861-en":3,"doc-seo-123861-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123861,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","A Novel FIG-LSTM Ensemble Machine Learning Technique for Currency Exchange Rate Forecasting - Conference Paper","Accurately predicting currency exchange rate behavior remains a key difficulty for forex stakeholders across trading, investment, and banking. This work introduces an ensemble model that integrates fuzzy information granules (FIG) with long short-term memory (LSTM) inside a gated recurrent unit (GRU) framework to improve forecasting quality. OHLC features and technical indicators are used as inputs, while the combined FIG and LSTM outputs feed a trained GRU for final predictions. Experiments on EUR/USD, USD/GBP, and USD/CAD one-day candles from 2019-08-01 to 2023-12-31 demonstrate superior results via RMSE, MAPE, MAE, and R².","A Novel FIG-LSTM Ensemble Machine Learning Technique for Currency Exchange Rate Forecasting  \nTemitope Alade  \nSchool of Computing Sciences University of East Anglia Norwich, UK  \n[T.Alade@uea.ac.uk](T.Alade@uea.ac.uk)  \nOgonna Okafor Dept. of Computer Science Nottingham Trent University  \nNottingham, UK [ogonna.okafor2022@my.ntu.ac.uk](ogonna.okafor2022@my.ntu.ac.uk)  \nAbstract—Accurately predicting currency exchange rate behaviour remains a major challenge for all stakeholders (e.g. traders, investment firms, banks, etc.) in the foreign exchange (forex) market. Developing machine learning models that offer more accurate and potentially more reliable predictions is identified as a critical objective for the forex market. To address this issue, this paper proposes an ensemble machine learning model that integrates fuzzy information granule (FIG) with long short-term memory (LSTM) in a gated recurrent unit (GRU) to achieve a better forex forecasting performance. The proposed model uses open, high, low, close (OHLC) data and relevant technical indicators such as moving average, bollinger bands,%b, bandwidth, moving average convergence divergence (MACD), relative strength index (RSI), and average true range (ATR) as inputs. The outputs of the combined FIG and LSTM models are passed into a trained GRU model to make the final forex prediction. To evaluate the predictive performance of the proposed model, experiments are conducted using one-day candles of three of the most traded currency pairs, EUR/USD, USD/GBP and USD/CAD from 01 August 2019 to 31 December 2023 data set. The proposed model shows better forecasting performance in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2 ) values when compared with conventional LSTM, FIG and GRU prediction models. The proposed FIG-LSTM model also outperforms a state-of-the-art GRU-LSTM hybrid prediction model.  \nIndex Terms—Machine learning, exchange rate forecasting, fuzzy time series, long short-term memory, fuzzy information granule, gated recurrent unit  \nI. INTRODUCTION  \nWith hundreds of currency combinations to choose from, the influence of economic and geopolitical events, as well as traders’ expectations on changing market conditions, the foreign exchange (forex) market is the largest and most volatile market in the world [1] . Accurately predicting currency exchange rate fluctuations is vital to all stakeholders (e.g. traders, investment firms, banks, etc.) to support investment decisions and policy making. A wide variety of exchange rate forecasting models have been proposed over the years using statistical methods such as the autoregressive integrated moving average (ARIMA) [2], structural models [3] and Bayesian theory based models [4] . A common problem with these approaches is the assumption that the time series being predicted is linear and stationary [5] . According to [6], the financial market is non-stationary and nonlinear. With increased availability of historical data, rise in computing power and advances in  \nmachine learning, the use of machine learning algorithms for forex market prediction has drawn considerable attention. Deep learning models such as long short-term memory (LSTM) and gated recurrent unit (GRU) have been explored in [7] and [8] respectively, and shown to produce better forex price prediction results than statistical models. Fuzzy logic techniques have been successfully used to perform price predictions in [9] and demonstrated comparable predictive accuracy to deep learning models and other machine learning algorithms. Machine learning techniques are able to model complex, nonlinear relationships in the data and can adjust to changing market conditions. More recently, hybrid forecasting approaches [10] which combine machine learning techniques with other approaches have shown to provide better forecasting performance than standalone techniques. Combining di","cbCaiplyAMc5bOKH","https://ap.wps.com/l/cbCaiplyAMc5bOKH","pdf",1644703,1,7,"English","en",105,"# Introduction\n## Motivation and challenges in forex forecasting\n## Related work and hybrid approaches\n# Machine learning techniques for forex price forecasting\n## Overview of ML methods","[{\"question\":\"What forecasting challenge does the paper address in the forex market?\",\"answer\":\"The paper targets the difficulty of accurately predicting currency exchange rate behavior amid non-stationary and nonlinear market dynamics.\"},{\"question\":\"How does the proposed FIG-LSTM ensemble model generate forecasts?\",\"answer\":\"It combines fuzzy information granules (FIG) with LSTM, then passes the combined outputs into a trained GRU to produce the final currency exchange rate prediction.\"},{\"question\":\"Which features and indicators are used as inputs, and how is performance evaluated?\",\"answer\":\"The model uses OHLC data plus technical indicators such as moving averages, Bollinger bands, bandwidth, MACD, RSI, and ATR; performance is measured using RMSE, MAPE, MAE, and R² against baseline LSTM, FIG, GRU, and a GRU-LSTM hybrid model.\"}]","A Novel FIG-LSTM Ensemble Machine Learning Technique for Currency Exchange Rate Forecasting - Conference Paper | PDF",1785818936,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-novel-fig-lstm-ensemble-machine-learning-technique-for-currency-exchange-rate-forecasting-conference-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-novel-fig-lstm-ensemble-machine-learning-technique-for-currency-exchange-rate-forecasting-conference-paper/123861/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What forecasting challenge does the paper address in the forex market?","Question",{"text":75,"@type":76},"The paper targets the difficulty of accurately predicting currency exchange rate behavior amid non-stationary and nonlinear market dynamics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed FIG-LSTM ensemble model generate forecasts?",{"text":80,"@type":76},"It combines fuzzy information granules (FIG) with LSTM, then passes the combined outputs into a trained GRU to produce the final currency exchange rate prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"Which features and indicators are used as inputs, and how is performance evaluated?",{"text":84,"@type":76},"The model uses OHLC data plus technical indicators such as moving averages, Bollinger bands, bandwidth, MACD, RSI, and ATR; 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