[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119603-en":3,"doc-seo-119603-105":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":4,"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},119603,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Predictive Analytics for Stock Prices Using Machine Learning Techniques - Dissertation","This dissertation examines predictive analytics for stock prices through advanced machine learning and statistical time-series modeling to support market trend analysis. ARIMA and Prophet are combined with unsupervised clustering using K-means to produce short-term forecasts and long-term trend insights. Large, worldwide stock-price datasets are used for training and testing with attention to both linear and nonlinear industry behaviors. Forecasting challenges are evaluated using MAE, RMSE, and MAPE, and results compare ARIMA’s stability with Prophet’s seasonality handling.","School of Mathematics, Statistics and Actuarial Science  \nMA981 DISSERTATION  \nPredictive Analytics for Stock Prices Using Machine Learning Techniques  \nRashi Chandel  \nSupervisor: Mr. Rishideep Roy  \nSeptember 16, 2024 Colchester  \nAbstract  \nThis dissertation examines the use of advanced methods of machine learning and statistical models that can be used to handle stock price prediction and market trend analysis. The ARIMA and Prophet models will be applied, together with unsupervised clustering approaches like K-means, to derive insight into the short-term prediction of stock prices and long-term trends. The researcher used huge stock price data from around the world to train and test their models, keeping special care for industries and stocks exhibiting both linear and nonlinear behaviour.  \nThis paper also informs in detail how time series forecasting is subjected to many challenges along with seasonality and volatility, even due to some external factors like holidays and big financial events. Each one is ranked according to the rigorous evaluation by MAE, RMSE, and MAPE metrics. The results bring out the strengths of ARIMA in stable market conditions and Prophet’s relative strengths in handling complexity with strong seasonality.  \nBesides time series forecasting, the current dissertation also applies a clustering analysis to group the stocks by their volatility and performance task giving investors amore profound insight into the risks and opportunities of the market. The results of this study have pointed out the fact that such a combination of statistical techniques with machine learning algorithms significantly improves the accuracy of stock price predictions and subsequently yields better decision-making by investors.  \nThe research has added to the ever-growing domain of financial forecasting in a way that has identified how practically viable machine learning models are in stock markets while simultaneously developing a skeleton for further research in predictive analytics.  \nAcknowledgment  \nI would like to begin by giving my major thanks to the Lord. Without his guidance, constant grace and light, surviving through this journey would not have been possible. This whole journey at every step has been a blessing, and for that, I shall always be grateful.  \nMummy and Papa, thank you for believing in me through all these years. Thankyou for the constant support and encouragement that have provided me with endless strength to move forward during such tough times. I have been pretty fortunate about having both of you with me. To my Sister Ashi, you have always been my rock on which, whenever there is any need for advice or just a word of encouragement, I could always count. Thank you for being my rock, my constant support system.  \nA word of gratitude to the best of my friends, Danish in fact, I would not have made it through the master’s without him. The friendship and support he has given me means a lot to me, and I can never thank him enough for having my back. To Nadeen, who has been there for the whole year, helping me through it all thank you for your patience, your kindness, and your friendship. And to Nimra, too, who has always been like an elder sister to me thank you for showing the way and keeping an eye out for me.  \nI am especially indebted to my supervisor, Mr. Rishideep Roy. His guidance and patience made a huge difference. You pushed me to grow, and for that, I’m grateful. Specially to University of Essex and all the staff members for being so supportive and helpful throughout the year.  \nTo all the strong, inspiring women who keep me going day in and day out, thankyou. Your strength, resilience, and passion have kept me constant to this moment, and as much as this achievement is mine, it is yours.  \nThanks from the bottom of my heart to everyone who has been a part of this journey.  \nContents  \n1 Introduction 8  \n2 Literature Review 12  \n2.1 Traditional Approaches to Stock Market Analysis ............","cbCailNUUdqKzvzv","https://ap.wps.com/l/cbCailNUUdqKzvzv","pdf",706415,1,56,"English","en",105,"# Introduction\n# Literature Review\n## Traditional Approaches to Stock Market Analysis\n## The Emergence of Time Series Models\n## Introduction of Machine Learning in Stock Market Prediction\n## AI-Driven Investment Strategies\n# Methodology\n## Data Collection and Preprocessing\n## Time Series Forecasting\n## Clustering Analysis\n## Comparison of Various Models\n## Methodological Limitations\n# Data Analysis and Findings\n## Exploratory Data Analysis\n## Time Series Forecasting\n## Clustering Analysis\n## Detailed Analysis: Sharpe Ratio","[{\"question\":\"Which forecasting models are used for stock price prediction?\",\"answer\":\"ARIMA and Prophet are applied for time series forecasting. Their performances are evaluated and compared using MAE, RMSE, and MAPE.\"},{\"question\":\"How does the dissertation incorporate machine learning beyond time series forecasting?\",\"answer\":\"It uses clustering analysis to group stocks based on volatility and performance, providing investors with deeper insight into market risks and opportunities.\"},{\"question\":\"What evaluation metrics and comparisons are used in the study?\",\"answer\":\"Model quality is ranked using MAE, RMSE, and MAPE. The results highlight ARIMA strengths under stable market conditions and Prophet strengths in handling complexity with strong seasonality.\"}]","Predictive Analytics for Stock Prices Using Machine Learning Techniques - Dissertation | PDF",1785725238,141,{"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},"predictive-analytics-for-stock-prices-using-machine-learning-techniques-dissertation","",{"@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/predictive-analytics-for-stock-prices-using-machine-learning-techniques-dissertation/119603/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which forecasting models are used for stock price prediction?","Question",{"text":75,"@type":76},"ARIMA and Prophet are applied for time series forecasting. Their performances are evaluated and compared using MAE, RMSE, and MAPE.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation incorporate machine learning beyond time series forecasting?",{"text":80,"@type":76},"It uses clustering analysis to group stocks based on volatility and performance, providing investors with deeper insight into market risks and opportunities.",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation metrics and comparisons are used in the study?",{"text":84,"@type":76},"Model quality is ranked using MAE, RMSE, and MAPE. The results highlight ARIMA strengths under stable market conditions and Prophet strengths in handling complexity with strong seasonality.","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"]