Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

2019 ◽  
Vol 26 (4) ◽  
pp. 164-174 ◽  
Author(s):  
Mahla Nikou ◽  
Gholamreza Mansourfar ◽  
Jamshid Bagherzadeh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Price ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Deep Learning Algorithm

scholarly journals Stock Price Prediction

2019 ◽  
Vol 9 (2S) ◽  
pp. 425-429
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Company Stock ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Technical And Fundamental Analysis ◽  
Future Value

Stock trading is a very crucial activity in the world of Finance and is a supporting structure for many companies. Predicting the future value of a stock is the main goal of stock price prediction project. In this paper, we have used machine learning algorithms to predict future stock prices of a company. Stock prediction by the stock brokers is mainly done using the time series or the technical and fundamental analysis but as these techniques are very unreliable and limited, we propose making use of intelligent techniques such as machine learning. Python is a programming language which can be used to implement machine learning algorithms with its numerous inbuilt libraries. We propose an approach that uses machine learning algorithms and will be trained on the historical stock data that is available and gain intelligence, later it uses the knowledge acquired for predicting the stock prices accurately. Random Forest Regression is one of the machine learning technique that is used for stock price prediction for small and large capitalizations also in different markets employing both up-to-minute and daily frequencies.

scholarly journals A Deep Learning Algorithm to Predict Hazardous Drinkers and the Severity of Alcohol-Related Problems Using K-NHANES

2021 ◽  
Vol 12 ◽  
Author(s):  
Suk-Young Kim ◽  
Taesung Park ◽  
Kwonyoung Kim ◽  
Jihoon Oh ◽  
Yoonjae Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Deep Learning Algorithm ◽  
Conventional Machine ◽  
Large Scale Survey ◽  
Alcohol Related Problems

Purpose: The number of patients with alcohol-related problems is steadily increasing. A large-scale survey of alcohol-related problems has been conducted. However, studies that predict hazardous drinkers and identify which factors contribute to the prediction are limited. Thus, the purpose of this study was to predict hazardous drinkers and the severity of alcohol-related problems of patients using a deep learning algorithm based on a large-scale survey data.Materials and Methods: Datasets of National Health and Nutrition Examination Survey of South Korea (K-NHANES), a nationally representative survey for the entire South Korean population, were used to train deep learning and conventional machine learning algorithms. Datasets from 69,187 and 45,672 participants were used to predict hazardous drinkers and the severity of alcohol-related problems, respectively. Based on the degree of contribution of each variable to deep learning, it was possible to determine which variable contributed significantly to the prediction of hazardous drinkers.Results: Deep learning showed the higher performance than conventional machine learning algorithms. It predicted hazardous drinkers with an AUC (Area under the receiver operating characteristic curve) of 0.870 (Logistic regression: 0.858, Linear SVM: 0.849, Random forest classifier: 0.810, K-nearest neighbors: 0.740). Among 325 variables for predicting hazardous drinkers, energy intake was a factor showing the greatest contribution to the prediction, followed by carbohydrate intake. Participants were classified into Zone I, Zone II, Zone III, and Zone IV based on the degree of alcohol-related problems, showing AUCs of 0.881, 0.774, 0.853, and 0.879, respectively.Conclusion: Hazardous drinking groups could be effectively predicted and individuals could be classified according to the degree of alcohol-related problems using a deep learning algorithm. This algorithm could be used to screen people who need treatment for alcohol-related problems among the general population or hospital visitors.

scholarly journals Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms

Computers ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 113
Author(s):  
James Coe ◽  
Mustafa Atay
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Racial Bias ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Design Development ◽  
Deep Learning Algorithm ◽  
The Impact

The research aims to evaluate the impact of race in facial recognition across two types of algorithms. We give a general insight into facial recognition and discuss four problems related to facial recognition. We review our system design, development, and architectures and give an in-depth evaluation plan for each type of algorithm, dataset, and a look into the software and its architecture. We thoroughly explain the results and findings of our experimentation and provide analysis for the machine learning algorithms and deep learning algorithms. Concluding the investigation, we compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most. We evaluate racial bias across five machine learning algorithms and three deep learning algorithms using racially imbalanced and balanced datasets. We evaluate and compare the accuracy and miss rates between all tested algorithms and report that SVC is the superior machine learning algorithm and VGG16 is the best deep learning algorithm based on our experimental study. Our findings conclude the algorithm that mitigates the bias the most is VGG16, and all our deep learning algorithms outperformed their machine learning counterparts.

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

scholarly journals Identification of Network Traffic over IOT Platforms

2021 ◽  
Vol 35 (4) ◽  
pp. 349-357
Author(s):  
Shilpa P. Khedkar ◽  
Aroul Canessane Ramalingam
Keyword(s):  
Machine Learning ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Heavy Traffic ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Deep Learning Algorithm ◽  
Iot Platforms ◽  
The Internet Of Things

The Internet of Things (IoT) is a rising infrastructure of 21st century. The classification of traffic over IoT networks is attained significance importance due to rapid growth of users and devices. It is need of the hour to isolate the normal traffic from the malicious traffic and to assign the normal traffic to the proper destination to suffice the QoS requirements of the IoT users. Detection of malicious traffic can be done by continuously monitoring traffic for suspicious links, files, connection created and received, unrecognised protocol/port numbers, and suspicious Destination/Source IP combinations. A proficient classification mechanism in IoT environment should be capable enough to classify the heavy traffic in a fast manner, to deflect the malevolent traffic on time and to transmit the benign traffic to the designated nodes for serving the needs of the users. In this work, adaboost and Xgboost machine learning algorithms and Deep Neural Networks approach are proposed to separate the IoT traffic which eventually enhances the throughput of IoT networks and reduces the congestion over IoT channels. The result of experiment indicates a deep learning algorithm achieves higher accuracy compared to machine learning algorithms.

scholarly journals Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab ◽  
Abhishek Dutta
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Short Term Memory ◽  
Efficient Market Hypothesis ◽  
Training Data ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of stock prices has been an important area of research for a long time. While supporters of the <i>efficient market hypothesis</i> believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. Researchers have also worked on technical analysis of stocks with a goal of identifying patterns in the stock price movements using advanced data mining techniques. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records from December 29, 2014 till December 28, 2018. Using these regression models, we predicted the <i>open</i> values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. Using the grid-searching technique, the hyperparameters of the LSTM models are optimized so that it is ensured that validation losses stabilize with the increasing number of epochs, and the convergence of the validation accuracy is achieved. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 <i>open</i> values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week's <i>open</i> value of the NIFTY 50 time series is the most accurate model.

scholarly journals Failure in Stock Price Prediction: A Comparison bettwen the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes of Existing Machine Learning Algorithms

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Ping Zhang ◽  
Jia-Yao Yang ◽  
Hao Zhu ◽  
Yue-Jie Hou ◽  
Yi Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Price ◽  
Financial Time Series ◽  
Learning Algorithms ◽  
Shape Feature ◽  
Curve Shape ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial Time

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

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