[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125463-en":3,"doc-seo-125463-105":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":20,"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},125463,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine Learning Approaches for Predicting Seasonal Stock Trends","The study addresses financial market volatility by focusing on the Indonesian transportation sector, where stock prices are strongly driven by recurring seasonal fluctuations. It evaluates machine learning algorithms for capturing seasonality in stock price prediction using monthly data for PT Garuda Indonesia (Persero) Tbk (GIAA.JK) from August 2019 to May 2025 from Yahoo Finance. Four models—Linear Regression, XGBoost, GRU, and LSTM—are trained and tested with RMSE, MAE, and MAPE, with hyperparameter tuning for XGBoost, LSTM, and GRU and Kruskal-Wallis validation. Tuned GRU achieves the best performance (MAE 5.90, RMSE 7.33, MAPE 9.67%), highlighting its effectiveness for both short-term dynamics and long-term seasonal dependencies.","Machine Learning Approaches for Predicting Seasonal Stock Trends  \nJason Miracle Gunawan*1, Christopher Andreas2, Theresia Ratih Putri3  \n1,2,3Program Studi Informatika, Fakultas Teknologi Informasi, Universitas Ciputra Surabaya,  \nSurabaya, Indonesia  \ne-mail: *[1](1jmiracle@student.ciputra.ac.id)[j](1jmiracle@student.ciputra.ac.id)[miracle@student.ciputra.ac.id](1jmiracle@student.ciputra.ac.id), [2](2theresia.ratih@ciputra.ac.id)[theresia.ratih@ciputra.ac.id](2theresia.ratih@ciputra.ac.id)  \nAbstrak  \nPasar keuangan sangat penting untuk pertumbuhan ekonomim tetapi sering mengalami volatilitas, terutama pada sektor transportasi Indonesia Dimana harga saham sangatdipengaruhi oleh flutuasi musiman. Metode peramalan konvensional sering mengabaikan pola berulang ini sehingga mengakibatkan rendahnya akurasi prediktif. Penelitian ini mengevaluasikemampuan algoritma Machine Learning untuk mengenali pola musiman pada prediksi hargasaham, dengan menggunakan data bulanan PT Garuda Indonesia (Persero) Tbk (GIAA.JK) dari Agustus 2019 hinggaMei 2025 yang didapatkan dari Yahoo Finance. Empat model, yaitu Linear Regression, Extreme Gradient Boosting (XGBoost), Gated Recurrent Unit (GRU), dan Long Short-Term Memory, dilatih dan diuji performanya dengan menggunakan Root Mean Square Error (RMSE), Mean Absolute Error (MAE), dan Mean Absolute Percentage Error (MAPE). Hyperparameter tuning diterapkanpada XGBoost, LSTM, dan GRU, sedangkan validasi statistik menggunakan tes Kruskal-Wallis. Hasil peneltian menunjukan model tuned GRU mengungguli model lain dengan MAE 5.90, RMSE 7.33, dan MAPE 9.67%, menunjukkan akurasi ‘excellence’dalam memodelkan dinamika jangka pendek dan musiman. Penemuan ini menekankan keunggulan GRUdalam memodelkanfluktuasi jangka pendek dan ketergantungan musim jangka Panjang dalam harga saham. Hasilnya memberikan ilmu praktis bagi investor dan menekankan pentingnya mengintegrasikan musiman dalam model prediktif untuk sektor yang fluktuatif.  \nKata kunci—Machine Learning, Gated Recurrent Unit, Prediksi Harga Saham, Analisis Pola Musiman, Sektor Transportasi Indonesia  \nAbstract  \nThe financial market is vital for economic growth yet it often experiences volatility, particularly in Indonesia’s transportation sector where stock prices are strongly affected by seasonal fluctuations. Conventional forecasting methods often neglect these recurring patterns, lowering predictive accuracy. This study assesses the capability of Machine Learning algorithms to capture seasonality in stock price prediction, using PT Garuda Indonesia (Persero) Tbk (GIAA.JK)’s monthly data from August 2019 to May 2025, retrieved from Yahoo Finance. Four models–Linear Regression, Extreme Gradient Boosting (XGBoost), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)–were trained and tested, with performance evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Hyperparameter tuning was applied to XGBoost, LSTM, and GRU, while statistical validation employed the Kruskal-Wallis test. Results showed that the tuned GRU outperformed other models, achieving MAE of 5.90, RMSE of 7.33, and MAPE of 9.67%, demonstrating ‘excellent’ accuracy in modelling both short-term and seasonal dynamics. These findings highlight the superiority of GRUin modelling both short-term fluctuations and long-term seasonal dependencies in stock prices. The results contribute practical insights for investors and emphasize the importance of integrating seasonality in predictive models for volatile sectors.  \nKeywords—Machine Learning, Gated Recurrent Unit, Stock Price Forecasting, Seasonal Pattern Analysis, Indonesia Transportation Sector  \n1. INTRODUCTION  \nFinancial market has a significant contribution in global economic by facilitating capital allocation and strengthening economic growth [1] . In fact, financial market can be one of the indicators of a country’s economy that all the financial experts are able to esti","cbCaiddwmiuWUpjt","https://ap.wps.com/l/cbCaiddwmiuWUpjt","pdf",632959,1,11,"English","en",105,"# Introduction\n## Background and Motivation\n# Methodology\n## Data and Study Period\n## Models and Evaluation Metrics\n## Hyperparameter Tuning and Statistical Validation\n# Results and Discussion\n## Model Performance Comparison\n## Interpretation of Seasonal Effects\n# Conclusion\n## Practical Implications for Investors","[{\"question\":\"Which machine learning models are compared for seasonal stock trend prediction?\",\"answer\":\"The study compares Linear Regression, XGBoost, GRU, and LSTM, trained on monthly stock data for PT Garuda Indonesia (Persero) Tbk (GIAA.JK).\"},{\"question\":\"What data is used, and what is the study period?\",\"answer\":\"Monthly data for PT Garuda Indonesia (Persero) Tbk (GIAA.JK) from August 2019 to May 2025 is used, retrieved from Yahoo Finance.\"},{\"question\":\"How is model performance evaluated and validated?\",\"answer\":\"Performance is evaluated using RMSE, MAE, and MAPE, with hyperparameter tuning applied to XGBoost, LSTM, and GRU and statistical validation using the Kruskal-Wallis test.\"}]","Machine Learning Approaches for Predicting Seasonal Stock Trends | PDF",1785899153,28,{"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},"machine-learning-approaches-for-predicting-seasonal-stock-trends","",{"@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/machine-learning-approaches-for-predicting-seasonal-stock-trends/125463/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are compared for seasonal stock trend prediction?","Question",{"text":75,"@type":76},"The study compares Linear Regression, XGBoost, GRU, and LSTM, trained on monthly stock data for PT Garuda Indonesia (Persero) Tbk (GIAA.JK).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data is used, and what is the study period?",{"text":80,"@type":76},"Monthly data for PT Garuda Indonesia (Persero) Tbk (GIAA.JK) from August 2019 to May 2025 is used, retrieved from Yahoo Finance.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated and validated?",{"text":84,"@type":76},"Performance is evaluated using RMSE, MAE, and MAPE, with hyperparameter tuning applied to XGBoost, LSTM, and GRU and statistical validation using the Kruskal-Wallis test.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]