Development and implementation of a predictive method for the stock market analysis, using the long short-term memory machine learning method

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.

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

Sensors ◽

10.3390/s21113678 ◽

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.

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Random forest and long short-term memory based machine learning models for classification of ion mobility spectrometry spectra

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXII ◽

10.1117/12.2585829 ◽

2021 ◽

Author(s):

Patrick C. Riley ◽

Samir V. Deshpande ◽

Brian S. Ince ◽

Brian C. Hauck ◽

Kyle P. O'Donnell ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Ion Mobility ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Machine Learning Models

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Chronological penguin Adam-based deep long short-term memory classifier for stock market prediction

International Journal of Intelligent Information and Database Systems ◽

10.1504/ijiids.2021.10037625 ◽

2021 ◽

Vol 14 (3) ◽

pp. 215

Author(s):

Dattatray P. Gandhmal ◽

K. Kumar

Keyword(s):

Stock Market ◽

Short Term Memory ◽

Short Term ◽

Stock Market Prediction ◽

Term Memory ◽

Long Short Term Memory

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Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

Nonlinear Processes in Geophysics ◽

10.5194/npg-27-373-2020 ◽

2020 ◽

Vol 27 (3) ◽

pp. 373-389 ◽

Cited By ~ 7

Author(s):

Ashesh Chattopadhyay ◽

Pedram Hassanzadeh ◽

Devika Subramanian

Keyword(s):

Neural Network ◽

Machine Learning ◽

Short Term Memory ◽

Data Driven ◽

Reservoir Computing ◽

Short Term ◽

Term Memory ◽

Machine Learning Methods ◽

Long Short Term Memory ◽

Lorenz 96

Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.

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