scholarly journals Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis

2022 ◽  
Vol 12 (2) ◽  
pp. 719
Author(s):  
Sibusiso T. Mndawe ◽  
Babu Sena Paul ◽  
Wesley Doorsamy
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Technical Analysis ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Linear Discriminant ◽  
Stock Price Prediction ◽  
Closing Price ◽  
Long Short Term Memory

Equity traders are always looking for tools that will help them maximise returns and minimise risk, be it fundamental or technical analysis techniques. This research integrates tools used by equity traders and uses them together with machine learning and deep learning techniques. The presented work introduces a South African-based sentiment classifier to extract sentiment from new headlines and tweets. The experimental work uses four machine learning models for fundamental analysis and six long short-term memory model architectures, including a developed encoder-decoder long short-term memory model for technical analysis. Data used in the experiments is mined and collected from news sites, tweets from Twitter and Yahoo Finance. The results from 2 experiments show an accuracy of 96% in predicting one of the major telecommunication companies listed on the JSE closing price movement while using the linear discriminant analysis model and an RMSE of 0.023 in predicting a significant telecommunication company closing price using encoder-decoder long short-term memory. These findings reveal that the sentiment feature contains an essential fundamental value, and technical indicators also help move closer to predicting the closing price.

scholarly journals Stock Market Predictor using Long short-term Memory (LSTM) Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 393-396
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Term Memory ◽  
Stock Price Prediction ◽  
Machine Learning Model ◽  
Long Short Term Memory

In the stock market, it is important to have accurate prediction of future behavior of stock price..Because of the great chance of financial loss as well as scoring profits at the same time, it is mandatory to have a secure prediction of the values of the stocks. But when it comes to predicting the value of a stock in future we tend to follow stock market experts but as technology is progressing we may use these technologies rather than following human experts who may be biased many times. Stock price prediction has been interesting area for investors and researchers. This article proposes an approach towards prediction of stock price using machine learning model Long Short Term Memory. This is an ensemble learning method that has been an exceedingly successful model for predicting sequence of numbers and words. Long Short Term Memory is a machine learning model for prediction. This technique is used to forecast the future stock price of a specific stock by using historical data of the stock gathered from Yahoo! Finance.

scholarly journals Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3678
Author(s):  
Dongwon Lee ◽  
Minji Choi ◽  
Joohyun Lee
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
The Other ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Attention Model ◽  
Long Short Term Memory ◽  
Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

scholarly journals Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different Variables

2021 ◽  
Vol 35 (4) ◽  
pp. 1167-1181
Author(s):  
Yun Bai ◽  
Nejc Bezak ◽  
Bo Zeng ◽  
Chuan Li ◽  
Klaudija Sapač ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Runoff Forecasting ◽  
Long Short Term Memory
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