[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-563128-105":53,"doc-detail-563128-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","forecasting-silver-prices-using-the-long-short-term-memory-lstm-method","Forecasting Silver Prices Using the Long Short-Term Memory (LSTM) Method","","Silver is a versatile metal widely used in industry, ornaments, and investment assets, making its price trend important for decision-making. Silver prices declined after the COVID-19 pandemic before recovering and stabilizing, motivating forecasting of short-term movements. This study applies the Long Short-Term Memory (LSTM) model to predict silver prices from January 2023 to March 2023 using historical data from January 2013 to January 2023, then evaluates performance with MAPE, achieving 0.0547.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/forecasting-silver-prices-using-the-long-short-term-memory-lstm-method/563128/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/forecasting-silver-prices-using-the-long-short-term-memory-lstm-method/563128.png","ImageObject",442,249,{"name":88,"@type":89},"8796093062539","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-29","2026-10-12",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",8,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"What forecasting approach does the study use for silver prices?","Question",{"text":109,"@type":110},"The study uses a Long Short-Term Memory (LSTM) model to forecast silver prices over a three-month period.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"Which time ranges are used for training and forecasting?",{"text":114,"@type":110},"Historical data from January 2013 to January 2023 is used, and predictions are made for January 2023 through March 2023.",{"name":116,"@type":107,"acceptedAnswer":117},"How is the forecasting model evaluated?",{"text":118,"@type":110},"Model performance is evaluated using Mean Absolute Percentage Error (MAPE), with a best obtained MAPE value of 0.0547.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},563128,1792093503,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":60,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":101,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":76},8796093062539,"Int. j. adv. multidisc. res. stud. 2025; 5(2):869-876  \nReceived: 01-02-2025  \nAccepted: 11-03-2025  \nForecasting Silver Prices Using the Long Short-Term Memory (LSTM) Method  \n1 Dian Kurniasari, 2 Stevanus Kenny, 3 Warsono, 4 Widiarti  \n1, 2, 3, 4 Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia Corresponding Author: Dian Kurniasari  \n\n| Abstract |  |\n| --- | --- |\n| Silver is a versatile metal with numerous applications in | whether they will recover and stabilize. The forecasting is |\n| human life, including industrial materials, ornaments, and | conducted using the Long Short-Term Memory (LSTM) |\n| investment assets. It serves as an alternative investment | method for a three-month period, from January 2023 to |\n| option due to its lower price compared to gold and its | March 2023. The model’s performance is evaluated using |\n| potential for value appreciation under specific market | the Mean Absolute Percentage Error (MAPE), with the best- |\n| conditions. Historically stable, silver prices experienced a | obtained MAPE value being 0.0547. The forecasting results |\n| decline following the COVID-19 pandemic. Consequently, | indicate that silver prices will experience a brief decline |\n| this study aims to forecast silver price trends to determine Keywords: Forecasting, LSTM, MAPE, Silver | before rebounding. |\n\n1. Introduction  \nThe rapid advancement of technology has led to a future increasingly reliant on technological developments, particularly in forecasting. Forecasting is a compelling and relevant issue that integrates the need for analyzing time series data and predicting the most probable future outcomes [1] .  \nSilver is one of the most renowned metals worldwide, serving multiple purposes, including its use as an alternative to gold in jewelry due to its relatively lower cost. Given its properties similar to gold, silver has also emerged as a competitive investment asset. Silver prices tend to remain stable and can appreciate under certain conditions. However, during the COVID- 19 pandemic, silver prices declined significantly before recovering over time, eventually stabilizing. Thus, forecasting silver prices is crucial for assessing its viability as a sound investment. This study aims at forecasting silver prices for the period from January 2023 until March 2023 using historical data from January 2013 until January 2023, [sourced from Investing.com](sourced from Investing.com).  \nTime series analysis offers various forecasting methods, including Auto-Regressive (AR), Moving Average (MA), and AutoRegressive Moving Average (ARMA) . Although linear statistical forecasting models are widely used, they often fail in providing accurate predictions for nonlinear time series data due to their underlying assumption of stationarity and linearity [2] . Recurrent Neural Networks (RNNs) have been introduced as a suitable forecasting approach for nonlinear time series data. However, RNNs struggle with long-term memory retention, leading to information loss over extended sequences. Long ShortTerm Memory (LSTM) networks, a variant of RNN, improve upon this limitation by incorporating gating mechanisms that selectively store and discard information, ensuring long-term dependency retention [3] . The three gates in LSTM—input gate, forget gate, and output gate—enable effective filtering and updating of information, making it well-suited for long-term time series forecasting, including silver price predictions.  \nPrevious studies have demonstrated the effectiveness of LSTM in various forecasting applications. Kumar et al. [4] applied LSTM for cloud datacenter forecasting, while Yadav et al. [5] used it in predicting the Indian stock market. Song et al. [6] employed LSTM in oil production forecasting, and Hong et al. [7] utilized it in air pollutant concentration prediction for managing port air quality. More recently, Pothuganti [8] applied LSTM in stock market exchange predictio","cbCainenmdrUOpbC","https://ap.wps.com/l/cbCainenmdrUOpbC","pdf",688886,"English","# Abstract\n# Introduction\n# Research Method\n## Time Series\n## Data Mining\n## Machine Learning","[{\"question\":\"What forecasting approach does the study use for silver prices?\",\"answer\":\"The study uses a Long Short-Term Memory (LSTM) model to forecast silver prices over a three-month period.\"},{\"question\":\"Which time ranges are used for training and forecasting?\",\"answer\":\"Historical data from January 2013 to January 2023 is used, and predictions are made for January 2023 through March 2023.\"},{\"question\":\"How is the forecasting model evaluated?\",\"answer\":\"Model performance is evaluated using Mean Absolute Percentage Error (MAPE), with a best obtained MAPE value of 0.0547.\"}]","Forecasting Silver Prices Using the Long Short-Term Memory (LSTM) Method | PDF",1791812353]