Application of Machine Learning Algorithms in Stock Market Prediction

2020 ◽  
pp. 153-180
Author(s):  
Sumit Kumar ◽  
Sanlap Acharya
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Story Point-Based Effort Estimation Model with Machine Learning Techniques

2020 ◽  
Vol 30 (01) ◽  
pp. 43-66
Author(s):  
Muaz Gultekin ◽  
Oya Kalipsiz
Keyword(s):  
Machine Learning ◽  
Empirical Evaluation ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Effort Estimation ◽  
Estimation Model ◽  
Software Projects ◽  
Learning Techniques ◽  
Better Than

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.

Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques

2019 ◽  
Vol 6 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Jai Prakash Verma ◽  
Sudeep Tanwar ◽  
Sanjay Garg ◽  
Ishit Gandhi ◽  
Nikita H. Bachani
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Time Window ◽  
Stock Exchange ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Trend Prediction ◽  
Stock Market Forecasting ◽  
Learning Techniques

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.

scholarly journals Myocardial Infarction Prediction Using Hybrid Machine Learning Techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 4251-4260
Author(s):  
Vaddi Niranjan Reddy Et.al
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Learning Classifier ◽  
Learning Techniques ◽  
Accuracy Performance ◽  
Graphical Presentation

The myocardial infarction prediction is an important task in health care domain in the current days. So, Prediction of cardiovascular diseases is a critical challenge in the area of clinical data analysis. It is difficult to predict myocardial infarction prediction by physicians with huge health records. To overcome this complexity we need to implement the automatic heard disease prediction system to notify the patient and get to recovery from the disease. Here to gaining the automatic system we are using machine learning techniques to easily performing myocardial infarction prediction. The machine learning techniques can be split into multiple types like unsupervised and supervised learning classifier. The supervised learning techniques working with structured data which is recommended to implement this classifiers. So, in this system we are using supervised learning techniques namely KNN, RF, NN, DT, NB, and SVM classifiers. To predict myocardial infarction, this system is using training dataset which is accessing from UCI ML repository. As well as this system is comparing accuracy performance between various machine learning algorithms and accuracy results with graphical presentation. This makes the accessing of the risk of the disease in the early stages and can try to save the patient without having any loss.

scholarly journals Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 528
Author(s):  
David Opeoluwa Oyewola ◽  
Emmanuel Gbenga Dada ◽  
Sanjay Misra ◽  
Robertas Damaševičius
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Short Term Memory ◽  
Multinomial Logistic Regression ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Noise Filter ◽  
Learning Techniques

The application of machine learning techniques to the epidemiology of COVID-19 is a necessary measure that can be exploited to curtail the further spread of this endemic. Conventional techniques used to determine the epidemiology of COVID-19 are slow and costly, and data are scarce. We investigate the effects of noise filters on the performance of machine learning algorithms on the COVID-19 epidemiology dataset. Noise filter algorithms are used to remove noise from the datasets utilized in this study. We applied nine machine learning techniques to classify the epidemiology of COVID-19, which are bagging, boosting, support vector machine, bidirectional long short-term memory, decision tree, naïve Bayes, k-nearest neighbor, random forest, and multinomial logistic regression. Data from patients who contracted coronavirus disease were collected from the Kaggle database between 23 January 2020 and 24 June 2020. Noisy and filtered data were used in our experiments. As a result of denoising, machine learning models have produced high results for the prediction of COVID-19 cases in South Korea. For isolated cases after performing noise filtering operations, machine learning techniques achieved an accuracy between 98–100%. The results indicate that filtering noise from the dataset can improve the accuracy of COVID-19 case prediction algorithms.

scholarly journals Stock Prediction Using Machine Learning

2021 ◽  
Vol 9 ◽  
pp. 152-158
Author(s):  
Shubha Singh ◽  
Sreedevi Gutta ◽  
Ahmad Hadaegh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

The Trend of stock price prediction is becoming more popular than ever. Share market is difficult to predict due to its volatile nature. There are no rules to follow to predict what will happen with the stock in the future. To predict accurately is a huge challenge since the market trend always keep changing depending on many factors. The objective is to apply machine learning techniques to predict stocks and maximize the profit. In this work, we have shown that with the help of artificial intelligence and machine learning, the process of prediction can be improved. While doing the literature review, we realized that the most effective machine learning tool for this research include: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Genetic Algorithms (GA). All categories have common and unique findings and limitations. We collected data for about 10 years and used Long Short-Term Memory (LSTM) Neural Network-based machine learning models to analyze and predict the stock price. The Recurrent Neural Network (RNN) is useful to preserve the time-series features for improving profits. The financial data High and Close are used as input for the model.

scholarly journals Rank Prediction for Articles and Conference Papersusing Machine Learning Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 9746-9750
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Supervised And Unsupervised Learning ◽  
Learning Techniques ◽  
Day By Day

Searching for an optimal article which was given highest and best priority is quite harder based on requirements. Ranking is one of the best measure or a method to get the best rated and optimal article or a conference or a research paper through this huge Internet World. As Technology been increasing day by day Artificial Intelligence is the first step to get through any problem for a solution Machine learning is also an important aspect of Artificial Intelligence. Machine Learning is best known for classifying, categorizing and predicting. Rank prediction can be done through many different algorithm implementations in machine learning. But choosing the best is important for accurate results. This paper gives the most accurate results of algorithms that can be used for rank predictions for articles. To simplify and resolve this problem, solutions were given in many different ways but to achieve accuracy is necessary, in previous models this is given using supervised learning only. We proposed this research work with perfect results using both supervised and unsupervised learning. Neural Networks is the best algorithm in supervised learning for classifying and predicting within data. In unsupervised learning we used K-means clustering because of grouping the data. This work helps the user(s) for optimal search of an article and also gives a competitive spirit for author to get into the top, totally this is implemented using Machine Learning Techniques of Neural Networks, K-Means Algorithm which is a mixture of supervised and unsupervised learning for predicting ranks.

scholarly journals Explainable stock prices prediction from financial news articles using sentiment analysis

2021 ◽  
Vol 7 ◽  
pp. e340
Author(s):  
Shilpa Gite ◽  
Hrituja Khatavkar ◽  
Ketan Kotecha ◽  
Shilpi Srivastava ◽  
Priyam Maheshwari ◽  
...  
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Stock Price Prediction ◽  
Financial News ◽  
Learning Techniques ◽  
High Level ◽  
Price Analysis

The stock market is very complex and volatile. It is impacted by positive and negative sentiments which are based on media releases. The scope of the stock price analysis relies upon ability to recognise the stock movements. It is based on technical fundamentals and understanding the hidden trends which the market follows. Stock price prediction has consistently been an extremely dynamic field of exploration and research work. However, arriving at the ideal degree of precision is still an enticing challenge. In this paper, we are proposing a combined effort of using efficient machine learning techniques coupled with a deep learning technique—Long Short Term Memory (LSTM)—to use them to predict the stock prices with a high level of accuracy. Sentiments derived by users from news headlines have a tremendous effect on the buying and selling patterns of the traders as they easily get influenced by what they read. Hence, fusing one more dimension of sentiments along with technical analysis should improve the prediction accuracy. LSTM networks have proved to be a very useful tool to learn and predict temporal data having long term dependencies. In our work, the LSTM model uses historical stock data along with sentiments from news items to create a better predictive model.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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