[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121946-en":3,"doc-seo-121946-105":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},121946,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","TIMESERIES FORECASTING OF U. S. HOUSING PRICE INDEX USING MACHINE LEARNING AND DEEP LEARNING MODELS","Time series forecasting supports future value and pattern prediction from historical data, enabling improved decisions across industries. The U.S. housing market strongly affects the economy, making the Housing Price Index (HPI) a key indicator published by government and private agencies. Multiple factors drive HPI, and advances in machine learning and deep learning better capture trends, seasonality, and long-term dependencies. This dissertation builds macroeconomic datasets correlated with HPI and standard economist features, then develops and compares traditional, ML, DL, and hybrid hybrid models using RMSE, MAE, and R-squared.","The University of Southern Mississippi  \nThe Aquila Digital Community  \nDissertations  \nFall 12-7-2023  \nTIMESERIES FORECASTING OF U.S. HOUSING PRICE INDEX USING MACHINE LEARNING AND DEEP LEARNING MODELS krishna chaitanya nunna  \nFollow this and additional works at: [https://aquila.usm.edu/dissertations](https://aquila.usm.edu/dissertations)  \n Part of the Computational Engineering Commons, and the Other Computer Engineering Commons  \nRecommended Citation  \nnunna, krishna chaitanya, \"TIMESERIES FORECASTING OF U.S. HOUSING PRICE INDEX USING MACHINE LEARNING AND DEEP LEARNING MODELS\" (2023) . Dissertations. 2181.  \n[https://aquila.usm.edu/dissertations/2181](https://aquila.usm.edu/dissertations/2181)  \nThis Dissertation is brought to you for free and open access by The Aquila Digital Community. It has been accepted for inclusion in Dissertations by an authorized administrator of The Aquila Digital Community. For more information, please contact [aquilastaff@usm.edu](aquilastaff@usm.edu).  \nTIMESERIES FORECASTING OF U. S. HOUSING PRICE INDEX USING MACHINE LEARNING AND DEEP LEARNING MODELS  \nby  \nKrishna Chaitanya Nunna  \nA Dissertation  \nSubmitted to the Graduate School,  \nthe College of Arts and Sciences  \nand the School of Computing Sciences and Computer Engineering at The University of Southern Mississippi in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy  \nApproved by:  \nDr. Zhaoxian Zhou, Committee Chair  \nDr. Chaoyang Zhang  \nDr. Bo Li  \nDr. Ras B. Pandey  \nDr. Sarbagya Ratna Shakya  \nDecember 2023  \nCOPYRIGHT BY  \nKrishna Chaitanya Nunna  \n2023  \nPublished by the Graduate School  \nABSTRACT  \nTime series forecasting is a promising technique for various applications which predicts future values or patterns by taking historical data as base. Forecasting future trends is very beneficial for different industries to make valuable decisions and strategies. One such industry is housing market; it has biggest influence on U.S. economy. Housing price index (HPI) is a one of the crucial economic indices published by various government funded and private agency to benefit several industries and individuals for better analysis of future trends of housing market.  \nSeveral factors influence the HPI, economical, geographical, and demographic features. Development of traditional time series forecasting models showed greater impact in capturing the trends and seasonality in predicting future values. Advancements in Machine Learning (ML) and Deep Learning (DL) architectures for forecasting using time series data makes the predictions more accurate with a smaller number of data instancesand using long term dependencies. Related work shows the prediction of housing market was composed to the local areas based on the geographics and characteristics of the house but not on nationwide economic indicators which makes bigger impact on the market.  \nIn our research, firstly we developed our time series dataset by collecting macroeconomic features which correlates mostly with the HPI from various economic and finance agencies. We also collected the standard dataset of features which predict HPI published by economists every year. Analyzed, visualized, and compared the collected data set with the standard dataset. Designed traditional time series models, machine learning and deep learning models for forecasting HPI using two datasets.  \nSecondly, compared the performance of the models using evaluation metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R- Squared (􀜴2) . We designed a hybrid architecture by coupling machine learning and deep learning models for better predictions of the targeted variable. Forecasted the future values of HPI using the best performing model.  \nACKNOWLEDGMENTS  \nFirstly, I would like to thank my esteemed supervisor, Professor Dr. Zhaoxian Zhou, for his invaluable guidance, support, and patience. His immense knowledge and mentorship have encouraged and inspired me in all th","cbCaihyXXJboaUct","https://ap.wps.com/l/cbCaihyXXJboaUct","pdf",2860148,1,109,"English","en",105,"# ABSTRACT\n# ACKNOWLEDGMENTS\n# DEDICATION\n# LIST OF TABLES\n# LIST OF IILUSTRATIONS\n# LIST OF ABBREVATIONS\n# CHAPTER I - INTRODUCTION","[{\"question\":\"为什么时间序列预测在住房市场分析中很重要？\",\"answer\":\"时间序列预测能基于历史数据预测未来趋势与模式，帮助行业与决策者制定更有效的策略。\"},{\"question\":\"HPI（住房价格指数）在研究中扮演什么角色？\",\"answer\":\"HPI是衡量住房市场的重要经济指标，由政府或机构发布，用于分析未来趋势并服务于相关行业与个人。\"},{\"question\":\"研究如何评估不同预测模型的表现？\",\"answer\":\"通过RMSE、MAE以及R-squared等评价指标比较传统模型、机器学习模型、深度学习模型以及混合架构的预测效果。\"}]","TIMESERIES FORECASTING OF U. S. 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