Prediction model for stock price trend based on recurrent neural network

2020 ◽  
Author(s):  
Jinghua Zhao ◽  
Dalin Zeng ◽  
Shuang Liang ◽  
Huilin Kang ◽  
Qinming Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Stock Price ◽  
Price Trend

Prediction model for stock price trend based on convolution neural network

2020 ◽  
Vol 39 (4) ◽  
pp. 4999-5008
Author(s):  
Hongbo Lin ◽  
Jinghua Zhao ◽  
Shuang Liang ◽  
Huilin Kang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Image Features ◽  
Image Feature ◽  
Extraction Ability ◽  
Residual Network ◽  
Trend Prediction ◽  
Prediction Ability ◽  
Price Trend

Aiming at the image features of stock data, considering the picture features of stock data and the characteristics of CNN’s good at extracting picture features, the paper proposed a stock price trend prediction model CNN-M based on a Convolutional Neural Network. At the same time, based on the excellent image feature extraction ability of the residual network, this paper proposed a residual network-based stock price trend prediction model ResNet-M based on the Conventional Neural Network. The experimental results showed that the prediction ability of the improved residual network-based prediction model Resnet-M is superior to the CNN model.

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

A novel model by evolving partially connected neural network for stock price trend forecasting

2012 ◽  
Vol 39 (1) ◽  
pp. 611-620 ◽  
Author(s):  
Pei-Chann Chang ◽  
Di-di Wang ◽  
Chang-le Zhou
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Price Trend ◽  
Trend Forecasting ◽  
Novel Model

scholarly journals Rating Player Actions in Soccer

2021 ◽  
Vol 3 ◽  
Author(s):  
Uwe Dick ◽  
Maryam Tavakol ◽  
Ulf Brefeld
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Performance Indicators ◽  
Data Driven ◽  
Tracking Data ◽  
Trajectory Prediction ◽  
Study Approach

We present a data-driven model that rates actions of the player in soccer with respect to their contribution to ball possession phases. This study approach consists of two interconnected parts: (i) a trajectory prediction model that is learned from real tracking data and predicts movements of players and (ii) a prediction model for the outcome of a ball possession phase. Interactions between players and a ball are captured by a graph recurrent neural network (GRNN) and we show empirically that the network reliably predicts both, player trajectories as well as outcomes of ball possession phases. We derive a set of aggregated performance indicators to compare players with respect to. to their contribution to the success of their team.

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