An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery

This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments

Stock Prediction Based on Social Media Data via Sentiment Analysis: a Study on Reddit

With the development of internet and information technology, online text data has become available and accessible for research in many fields including stock prediction. Social media, being one of the biggest content generators on the internet, is a great data resource for text mining and stock prediction. It has a large capacity, high data density, and fast information spread. In this thesis, analyses on the relationship between the stock-related text in social media (Reddit) and the price changes of corresponding stocks are implemented. In the analysis, sentiment analysis is first applied to extract the individual users’ emotions and opinions about the stocks. After that, the extracted features are analyzed via descriptive statistics and predictive analysis using the Pearson correlation coefficient and machine learning models. The predictive analysis is designed to examine the dependence between the social media text data and stock price change by evaluating the performance of predictions, four indicators are used in the evaluation including “prediction accuracy on price change direction” and three indicators in simulated algorithm trading experiments based on prediction results. They are “total profit with trading strategy for single stock”, “daily profit efficiency of trading strategy” and “total profit with Portfolio trading strategy”. From the results and the comparison with a Buy and Hold (B&H) baseline strategy, the predictions show good results in terms of “daily profit efficiency” and “total profit with Portfolio trading strategy”. Therefore, the online forum text from Reddit are proved to be correlated with future stock price changes and might be used to make more profit than B&H strategy by incorporating their information in portfolio trading strategies

Intelligent system for associative pattern identification in data

Mestrado em Gestão de Sistemas de InformaçãoOs resultados das ferramentas estatísticas são baseados em resultados numéricos onde a interpretação e compreensão do que está gerado passa pelo intérprete que está a analisar os resultados. Esta tarefa de compreensão é muitas vezes complicada por vários fatores sendo um dos quais o facto do intérprete não conseguir captar dos resultados o que é relevante para avaliar o modelo formulado, não conseguindo avalia-lo como válido ou não, o que poderá levar à utilização de modelos que podem ser descabidos e sem fundamento. Com esta ideia em consideração foi desenvolvido, em ambiente Linux, um pequeno sistema com técnicas de data mining de carácter associativo. Neste sistema é gerado um relatório por cada modelo, onde são analisados os fatores mais relevantes para a criação de modelos, guiando desta forma o intérprete a decidir validar e utilizar o modelo criado ou a rejeitá-lo. O objetivo deste trabalho passou pela aprendizagem da linguagem Python aplicado a dados, uma aprendizagem aprofundada sobre data mining, as técnicas e métodos existentes e uma verificação das ferramentas de machine learning, de modo a criar como produto final um sistema com algumas técnicas. Foi possível a realização do trabalho proposto com a criação do sistema. Foram formulados métodos para produzir um modelo de regressão linear múltipla, regressão logística, um modelo de correlação linear e um modelo de regras de associação. Para três modelos foram gerados métodos tendo por base bibliotecas e machine learning. Para as regras de associação foi criado um método de raiz baseado no algoritmo FP-Growth.For many people statistics is a difficult task to be done, where the output that is given from the analytic tools can be complicated to understand. With this idea it was investigated the possibility of creation of a system that provides the creation some models to the users where is provided some guidelines about the most important values to take care for each model. The goals of this project are the development of the knowledge about Data Mining, learn how to use Python to produce data analysis, verify the existent machine learning applied to data for Python and use some data mining techniques to create a small system for associative models. The system is capable to perform a Linear Regression, a Logistic Regression, a Correlation Coefficient and an Association Rule Mining algorithm. For each method is provided an output that contains the numerical results of the method and it was produce some guidelines with general ideas, assumptions of each method and it is interpreted the most important statistical values to facilitate the understanding of all the methods. The system was developed in Python. Three methods were created are based on machine learning algorithms. The association rule mining algorithm was created from the beginning. The association rule mining algorithm developed was FP-growth. The system was ready to run in Linux.info:eu-repo/semantics/publishedVersio

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Англійська мова для навчання і работи. Навчальний посібник з англійської мови за професійним спрямуванням для студентів і фахівців галузі знань 0503 Розробка корисних копалин Т 1

A coursebook includes all the activities of students’ work at ESP course aimed at development of language behaviour necessary for effective communication of students in their study and specialism areas. The tasks and activities given in the coursebook are typicalfor students’ academic and professional domains and situations. The content is organized in modules that covers generic job-related language skills of engineers. The authentic texts taken from real life contain interesting up-to-date information about mining, peculiarities of study abroad, customs and traditions of English-speaking countries. Pack of self-study resources given in Part II contains Glossary of mining terms, tasks and activities aimed at developing a range of vocabulary necessary for mining, different functions and functional exponents to be used in academic and professional environment as well as tasks developing self-awareness, self-assessment and self-organisation skills. Testing points for different grammar structuresare given in Part III. Indices at the end of each part easify the use of the coursebook. The coursebook contains illustrations, various samples of visualizing technical information. The coursebook is designed for ESP students of non-linguistic universities. It can be used as teaching/learning materials for ESP Courses for Mining Engineers as well as for self-study of subject and specialist teachers, practicing mining engineers and researchers in Engineering.У посібнику представлені всі види діяльності студентів з вивчення англійської мови, спрямовані на розвиток мовної поведінки, необхідної для ефективного спілкування в академічному та професійному середовищах. Навчальний посібник містить завдання і вправи, типові для різноманітних академічних та професійних сфер і ситуацій. Структура організації змісту– модульна і охоплює загальні мовленнєві вміння інженерів. Зразки текстів– автентичні, взяті з реального життя, містять цікаву та актуальну інформацію про видобувничу промисловість, особливості навчання за кордоном, традиції та звичаї країн, мова яких вивчається. Ресурси для самостійної роботи(Том ІІ) містять глосарій термінів, завдання та вправи для розвитку словарного запасу та розширення діапазону функціональних зразків, необхідних для виконання певних функцій, та завдання, які спрямовані на розвиток навичок самооцінювання і організації свого навчання. Граматичні явища і вправи для їх засвоєння наводяться в томі ІІІ. Наприкінці кожної частини наведено алфавітно-предметні покажчики. Багато ілюстрацій та різних візуальних засобів подання інформації. Навчальний посібник призначений для студентів технічних університетів гірничого профілю. Може використовуватися для самостійного вивчення англійської мови викладачами, фахівцями і науковцями різних інженерних галузей