東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Ensemble and Regression Algorithms f...

de Vasconcelos Feijao Filho, Romulo,

FindBook

Google Book

Amazon

博客來

Ensemble and Regression Algorithms for Stock Market Forecasting in Pre-Pandemic and Pandemic Scenarios /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Ensemble and Regression Algorithms for Stock Market Forecasting in Pre-Pandemic and Pandemic Scenarios // Romulo de Vasconcelos Feijao Filho.
作者:	de Vasconcelos Feijao Filho, Romulo,
面頁冊數:	1 electronic resource (28 pages)
附註:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
標題:	Forecasting. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31014312
ISBN:	9798382568959

Ensemble and Regression Algorithms for Stock Market Forecasting in Pre-Pandemic and Pandemic Scenarios /
de Vasconcelos Feijao Filho, Romulo,

Ensemble and Regression Algorithms for Stock Market Forecasting in Pre-Pandemic and Pandemic Scenarios /Romulo de Vasconcelos Feijao Filho. - 1 electronic resource (28 pages)

Source: Masters Abstracts International, Volume: 85-11.

Understanding and forecasting stock prices can be extremely profitable for people and useful for governments, specially in pandemic and pre-pandemic scenarios. The development of a machine learning model that can predict market behavior is the ultimate challenge. Therefore, many studies have been applying machine learning models in the context of stock prices forecasting. Model and feature selection is extremely important for any machine learning problem, so comparative studies are extremely important. In this work a comparative study is developed to evaluate tuned ensemble and regression models for predicting future stock prices.

English

ISBN: 9798382568959Subjects--Topical Terms:

547120
Forecasting.

Ensemble and Regression Algorithms for Stock Market Forecasting in Pre-Pandemic and Pandemic Scenarios /
LDR:01981nmm a22003973i 4500 001 2434672
005 20250923060939.5
006 m o d
007 cr|nu||||||||
008 261011s2022 miu||||||m |||||||eng d
020 $a 9798382568959
035 $a (MiAaPQD)AAI31014312
035 $a (MiAaPQD)Portugal10362154025
035 $a AAI31014312
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a de Vasconcelos Feijao Filho, Romulo, $e author. $3 3824733
245 1 0 $a Ensemble and Regression Algorithms for Stock Market Forecasting in Pre-Pandemic and Pandemic Scenarios / $c Romulo de Vasconcelos Feijao Filho.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 1 electronic resource (28 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-11.
500 $a Advisors: Rodrigues, Paulo Manuel Marques.
502 $b M.S. $c Universidade NOVA de Lisboa (Portugal) $d 2022.
520 $a Understanding and forecasting stock prices can be extremely profitable for people and useful for governments, specially in pandemic and pre-pandemic scenarios. The development of a machine learning model that can predict market behavior is the ultimate challenge. Therefore, many studies have been applying machine learning models in the context of stock prices forecasting. Model and feature selection is extremely important for any machine learning problem, so comparative studies are extremely important. In this work a comparative study is developed to evaluate tuned ensemble and regression models for predicting future stock prices.
546 $a English
590 $a School code: 7029
650 4 $a Forecasting. $3 547120
650 4 $a Pandemics. $3 3464080
650 4 $a Support vector machines. $3 2058743
650 4 $a Computer science. $3 523869
650 4 $a Epidemiology. $3 568544
650 4 $a Finance. $3 542899
690 $a 0800
690 $a 0984
690 $a 0766
690 $a 0508
710 2 $a Universidade NOVA de Lisboa (Portugal). $e degree granting institution. $3 3767537
720 1 $a Rodrigues, Paulo Manuel Marques $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-11.
790 $a 7029
791 $a M.S.
792 $a 2022
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31014312