Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.3390/e22080840 http://scihub22266oqcxt.onion/10.3390/e22080840 33286613!7517440!33286613     free     free     free       Entropy+(Basel) 2020 ; 22 (8): ä Nephropedia Template TP {{tp|p=33286613|t=2020. Deep Learning for Stock Market Prediction |pdf=|usr=}}{{33286613}} gab.com Text Deep Learning for Stock Market Prediction JO: Entropy (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33286613 #FOAvip The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost. Twit Text FOAVip Deep Learning for Stock Market Prediction ????Entropy (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33286613 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33286613 #FOAvip English Wikipedia <ref>{{Cite pmid|33286613}}</ref> Deep Learning for Stock Market Prediction #MMPMID33286613 Nabipour M; Nayyeri P; Jabani H; Mosavi A; Salwana E; S S Entropy (Basel) 2020[Jul]; 22 (8): ä PMID33286613show ga The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost. äDeep Learning for Stock Market Prediction (epmc).pdf DeepDyve Pubget Overpricing