[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-196989-105":53,"doc-detail-196989-en":126},{"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":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","stock-price-prediction-models-analysis","Stock Price Prediction Models Analysis","","This document presents a comparative analysis of various machine learning models for stock price prediction. It details parameter configurations for XGBoost, LSTM, and GRU models, including learning rates, number of estimators, max depth, units, dropout rates, dense units, and activation functions. The analysis includes performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) for Linear Regression, XGBoost, Tuned XGBoost, LSTM, Tuned LSTM, GRU, and Tuned GRU models. Notably, the tuned GRU model demonstrates superior performance with the lowest RMSE (0.51), MAE (0.26), and MAPE (0.42%), indicating a significant advantage over other tested models. The document also touches upon predicted stock prices and their corresponding RMSE values, further supporting the evaluation of predictive accuracy. This comprehensive evaluation aids in selecting the most effective model for stock market forecasting, highlighting the strengths of recurrent neural networks like GRU in capturing complex time-series patterns for financial data analysis.",{"@graph":63,"@context":118},[64,80,101],{"@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/stock-price-prediction-models-analysis/196989/",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/stock-price-prediction-models-analysis/196989.png","ImageObject",442,249,{"name":88,"@type":89},"Lute","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-26","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"Which model performed best in the stock price prediction analysis?","Question",{"text":108,"@type":109},"The tuned GRU model demonstrated the best performance, achieving the lowest RMSE, MAE, and MAPE values among all tested models.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What are the key performance metrics used in this analysis?",{"text":113,"@type":109},"The analysis used Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) to evaluate the accuracy of the stock price prediction models.",{"name":115,"@type":106,"acceptedAnswer":116},"What parameters were tuned for the LSTM and GRU models?",{"text":117,"@type":109},"For LSTM and GRU models, parameters such as Units_1, Units_2, Dropout, Dense_units, Activation, and Learning_rate were tuned to optimize performance.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},196989,1788464152,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"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":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":79},137454149569,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","| Model | Parameter | Description |\n| --- | --- | --- |\n| XGBoost | Learning_rate | [0.0001, 0.001, 0.01] |\n|  | N_estimators | [100, 200, 300] |\n|  | Max_depth | [3, 5, 7] |\n| LSTM | Units_ 1 | [64, 128, 192, 256] |\n|  | Units_2 | [32, 64, 96, 128] |\n|  | Dropout | [0.2, 0.3, 0.4, 0.5] |\n|  | Dense_units | [9, 25, 40, 56] |\n|  | Activation | [relu, tanh, leaky_relu] |\n|  | Learning_rate | [0.001, 0.0005, 0.0001] |\n| GRU | Units_ 1 | [64, 128, 192, 256] |\n|  | Units_2 | [32, 64, 96, 128] |\n|  | Dropout | [0.2, 0.3, 0.4, 0.5] |\n|  | Dense_units | [9, 25, 40, 56] |\n|  | Activation | [relu, tanh, leaky_relu] |\n|  | Learning_rate | [0.001, 0.0005, 0.0001] |\n\n| Model | RMSE | MAE | MAPE |\n| --- | --- | --- | --- |\n| Linear Regression | 7.46 | 55.60 | 70.11% |\n| XGBoost | 6.70 | 44.87 | 63.71% |\n| Tuned XGBoost | 7.75 | 60.03 | 84.29% |\n| LSTM | 5.98 | 35.70 | 55.79% |\n| Tuned LSTM | 6.69 | 44.79 | 70.03% |\n| GRU | 0.89 | 0.79 | 1.25% |\n| Tuned GRU | 0.51 | 0.26 | 0.42% |\n\n\n| Model | RMSE | MAE | MAPE |\n| --- | --- | --- | --- |\n| Linear Regression | 7.46 | 55.60 | 70.11% |\n| XGBoost | 6.70 | 44.87 | 63.71% |\n| Tuned XGBoost | 7.75 | 60.03 | 84.29% |\n\n\n| Predicted Stock Price | RMSE |\n| --- | --- |\n| 56 | 71 |\n| 58 | 55 |\n| 59 | 62 |\n| 58 | 56 |\n| 57 | 50 |\n| 55 | 60 |","cbCaiuqFOoII41xL","https://ap.wps.com/l/cbCaiuqFOoII41xL","pdf",625956,10,"English","# XGBoost Parameters\n## XGBoost Performance Metrics\n\n# LSTM Parameters\n## LSTM Performance Metrics\n\n# GRU Parameters\n## GRU Performance Metrics","[{\"question\":\"Which model performed best in the stock price prediction analysis?\",\"answer\":\"The tuned GRU model demonstrated the best performance, achieving the lowest RMSE, MAE, and MAPE values among all tested models.\"},{\"question\":\"What are the key performance metrics used in this analysis?\",\"answer\":\"The analysis used Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) to evaluate the accuracy of the stock price prediction models.\"},{\"question\":\"What parameters were tuned for the LSTM and GRU models?\",\"answer\":\"For LSTM and GRU models, parameters such as Units_1, Units_2, Dropout, Dense_units, Activation, and Learning_rate were tuned to optimize performance.\"}]","Stock Price Prediction Models Analysis | PDF"]