Using Machine Learning Algorithms on Prediction of Stock Price

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.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 4

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Full Dataset ◽

Learning Techniques ◽

Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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COMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS

DYNA INGENIERIA E INDUSTRIA ◽

10.6036/10007 ◽

2021 ◽

Vol 96 (5) ◽

pp. 528-533

Author(s):

XAVIER LARRIVA NOVO ◽

MARIO VEGA BARBAS ◽

VICTOR VILLAGRA ◽

JULIO BERROCAL

Keyword(s):

Machine Learning ◽

Deep Learning ◽

High Performance ◽

New Technologies ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Short Term ◽

Term Memory ◽

Learning Techniques ◽

Long Short Term Memory

Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long-short-term-memory. Additionally, this document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.

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Application of Machine Learning Algorithms in Stock Market Prediction

Handbook of Research on Smart Technology Models for Business and Industry - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3645-2.ch007 ◽

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.

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Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

10.21203/rs.3.rs-55617/v1 ◽

2020 ◽

Author(s):

Anjir Ahmed Chowdhury ◽

Khandaker Tabin Hasan ◽

Khadija Kubra Shahjalal Hoque

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Fuzzy Inference ◽

Machine Learning Techniques ◽

Short Term ◽

Inference System ◽

Learning Techniques ◽

Neuro Fuzzy ◽

The World ◽

Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named \newline SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping further spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can predict on small dataset with higher accuracy.Methods: In this research, we have used the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75. Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

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Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

10.21203/rs.3.rs-55617/v4 ◽

2020 ◽

Author(s):

Anjir Ahmed Chowdhury ◽

Khandaker Tabin Hasan ◽

Khadija Kubra Shahjalal Hoque

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Fuzzy Inference ◽

Machine Learning Techniques ◽

Short Term ◽

Inference System ◽

Learning Techniques ◽

Neuro Fuzzy ◽

The World ◽

Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping fur- ther spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can pre- dict on small dataset with higher accuracy.Methods: In this research, we have used the Adap- tive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75.Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

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The Sliding Window and SHAP Theory—An Improved System with a Long Short-Term Memory Network Model for State of Charge Prediction in Electric Vehicle Application

Energies ◽

10.3390/en14123692 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3692

Author(s):

Xinyu Gu ◽

KW See ◽

Yunpeng Wang ◽

Liang Zhao ◽

Wenwen Pu

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Sliding Window ◽

Processing Technique ◽

State Of Charge ◽

Machine Learning Techniques ◽

Short Term ◽

Learning Techniques ◽

Memory Network ◽

Long Short Term Memory

The state of charge (SOC) prediction for an electric vehicle battery pack is critical to ensure the reliability, efficiency, and life of the battery pack. Various techniques and statistical systems have been proposed in the past to improve the prediction accuracy, reduce complexity, and increase adaptability. Machine learning techniques have been vigorously introduced in recent years, to be incorporated into the existing prediction algorithms, or as a stand-alone system, with a large amount of recorded past data to interpret the battery characteristics, and further predict for the present and future. This paper presents an overview of the machine learning techniques followed by a proposed pre-processing technique employed as the input to the long short-term memory network (LSTM) algorithm. The proposed pre-processing technique is based on the time-based sliding window algorithm (SW) and the Shapley additive explanation theory (SHAP). The proposed technique showed improvement in accuracy, adaptability, and reliability of SOC prediction when compared to other conventional machine learning models. All the data employed in this investigation were extracted from the actual driving cycle of five different electric vehicles driven by different drivers throughout a year. The computed prediction error, as compared to the original SOC data extracted from the vehicle, was within the range of less than 2%. The proposed enhanced technique also demonstrated the feasibility and robustness of the prediction results through the persistent computed output from a random selection of the data sets, consisting of different driving profiles and ambient conditions.

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Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

10.21203/rs.3.rs-55617/v2 ◽

2020 ◽

Author(s):

Anjir Ahmed Chowdhury ◽

Khandaker Tabin Hasan ◽

Khadija Kubra Shahjalal Hoque

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Fuzzy Inference ◽

Machine Learning Techniques ◽

Short Term ◽

Inference System ◽

Learning Techniques ◽

Neuro Fuzzy ◽

The World ◽

Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping fur- ther spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can pre- dict on small dataset with higher accuracy.Methods: In this research, we have used the Adap- tive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE6.55 and Correlation Coefficient 0.75.Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

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

Information ◽

10.3390/info12120528 ◽

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.

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Stock Prediction Using Machine Learning

WSEAS TRANSACTIONS ON COMPUTER RESEARCH ◽

10.37394/232018.2021.9.17 ◽

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.

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Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method

Teknika ◽

10.34148/teknika.v10i1.328 ◽

2021 ◽

Vol 10 (1) ◽

pp. 62-67

Author(s):

Faisal Dharma Adhinata ◽

Diovianto Putra Rakhmadani

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Learning Approach ◽

Short Term ◽

Term Memory ◽

Learning Techniques ◽

Long Short Term Memory ◽

The Impact

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.

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