[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121255-en":3,"doc-seo-121255-105":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},121255,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Machine Learning Approaches to Airfare Prediction - A Comparative Study with Hybrid Models","Due to dynamic airline pricing and shifting market conditions, airfare costs fluctuate significantly and are hard for travelers to forecast using common knowledge, making it challenging to identify the best time to book. This bachelor’s thesis investigates machine learning and deep learning methods for airfare prediction, using an Indian domestic flight dataset enhanced with external signals such as fuel price and a holiday indicator in both tabular and time-series formats. Models including Random Forest, XGBoost, GRU, LSTM, and BiLSTM are evaluated using MAE and RMSE, while a hybrid GRU-XGBoost model with a meta-learning pipeline achieves the strongest performance.","MACHINE LEARNING APPROACHES TO AIRFARE PREDICTION: A COMPARATIVE STUDY WITH HYBRID MODELS  \nLappeenranta–Lahti University of Technology LUT  \nBachelor’s Programme in Software and Systems Engineering, Bachelor's thesis 2025  \nJiawei Sun  \nExaminer: Post-doctoral Researcher, Saddam Mukta  \nABSTRACT  \nLappeenranta–Lahti University of Technology LUTLUT School of Engineering Sciences  \nSoftware Engineering  \nHebei University of Technology HEBUT  \nJiawei Sun  \nMachine Learning Approaches to Airfare Prediction: A Comparative Study with Hybrid Models  \nBachelor’s thesis  \n2025  \n38 pages, 6 figures, and 9 tables  \nExaminer: Post-doctoral Researcher, Saddam Mukta  \nKeywords: airfare prediction, machine learning, deep learning, hybrid model, meta-learning, feature engineering, price optimization  \nDue to the dynamic pricing strategy and market conditions, the price of airfare fluctuates largely and is difficult to predict based on common knowledge by customers, making it difficult for travelers to find the best time to order a flight ticket a challenge. This thesis explores machine learning and deep learning approaches on airfare prediction to achieve better performance. A real-world dataset consisting of Indian domestic flight data integrated with extra external features such as fuel price and holiday flag in both tabular and time series versions were utilized in the thesis. Machine learning and deep learning models including Random Forest, XGBoost, GRU, LSTM and BiLSTM were evaluated based on MAE, RMSE and 􀀡!. A hybrid GRU-XGBoost model and a meta-learning pipeline outperformed others, with the best R² scores of 0.9469 (for tabular data) and 0.6938 (for time series data) . The results showed the effectiveness of applying hybrid-architecture and structured features in airfare prediction.  \nSYMBOLS AND ABBREVIATIONS  \nRoman characters  \np Ticket price (target variable) INR  \nt Time step in sequence models days  \nx Feature vector  \ny Ground truth airfare price INR  \n Predicted airfare price INR ∆􀀥 Price delta between days INR  \n􀀦 \" 3-day rolling mean of ticket prices INR  \n􀀧\"\\# 30-day rolling standard deviation INR  \nGreek characters  \na learning rate (in ML/DL models)λ Regularization parameter  \n􀀨 Model parameter vector  \nAbbreviations  \nAI Artificial Intelligence  \nAPI Application Programming Interface  \nDL Deep Learning  \nGRU Gated Recurrent Unit  \nLSTM Long Short-Term Memory  \nMAE Mean Absolute Error  \nML Machine Learning  \nMSE  \nPLS  \nRNN  \nRMSE  \nXGBoost  \nMean Square Error  \nPartial Least Squares Recurrent Neural Network Root Mean Squared Error Extreme Gradient Boosting  \nTable of contents  \nAbstract  \nSymbols and abbreviations  \n1 Introduction .................................................................................................................... 2  \n2 Literature review ............................................................................................................ 3  \n2.1 Airfare Price Determinants ..................................................................................... 3  \n2.2 Existing Price Tracking Tools.................................................................................4  \n2.3 Methods for Airfare Price Prediction ...................................................................... 5  \n3 Methodology .................................................................................................................. 8  \n3.1 Data Collection and Preprocessing ......................................................................... 8  \n3.2 Data Representation Formats ................................................................................ 10  \n3.3 Data Analysis ........................................................................................................ 12  \n3.4 Modelling Approach ............................................................................................. 13  \n3.4.1 Modelling with Tabular Data....................................................","cbCaiurcCGewjjLQ","https://ap.wps.com/l/cbCaiurcCGewjjLQ","pdf",1842835,1,38,"English","en",105,"# Table of contents\n## Abstract\n## Symbols and abbreviations\n## 1 Introduction\n## 2 Literature review\n## 3 Methodology\n## 4 Results\n## 5 Discussion\n## 6 Conclusions\n## References","[{\"question\":\"Why is airfare prediction difficult for travelers?\",\"answer\":\"Airfare pricing follows dynamic strategies and changes with market conditions, causing large fluctuations that are not reliably predictable from general knowledge.\"},{\"question\":\"What dataset and external features are used in the thesis?\",\"answer\":\"The study uses a real-world Indian domestic flight dataset, augmented with external factors such as fuel price and a holiday flag, represented in both tabular and time-series forms.\"},{\"question\":\"Which models are compared and what evaluation metrics are used?\",\"answer\":\"The thesis evaluates Random Forest, XGBoost, GRU, LSTM, and BiLSTM using MAE and RMSE, and compares performance across tabular and time-series setups.\"}]","Machine Learning Approaches to Airfare Prediction - A Comparative Study with Hybrid Models | PDF",1785734676,96,{"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-to-airfare-prediction-a-comparative-study-with-hybrid-models","",{"@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-to-airfare-prediction-a-comparative-study-with-hybrid-models/121255/",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","2026-08-03",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},"Why is airfare prediction difficult for travelers?","Question",{"text":75,"@type":76},"Airfare pricing follows dynamic strategies and changes with market conditions, causing large fluctuations that are not reliably predictable from general knowledge.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and external features are used in the thesis?",{"text":80,"@type":76},"The study uses a real-world Indian domestic flight dataset, augmented with external factors such as fuel price and a holiday flag, represented in both tabular and time-series forms.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models are compared and what evaluation metrics are used?",{"text":84,"@type":76},"The thesis evaluates Random Forest, XGBoost, GRU, LSTM, and BiLSTM using MAE and RMSE, and compares performance across tabular and time-series setups.","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"]