Stock Price Prediction A Referential Approach On How To Predict The Stock Price Using Simple Time Series
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Stock price Prediction a referential approach on how to predict the stock price using simple time series...
This book is about the various techniques involved in the stock price prediction. Even the people who are new to this book, after completion they can do stock trading individually with more profit.
Grammar-Based Feature Generation for Time-Series Prediction
Author: Anthony Mihirana De Silva
language: en
Publisher: Springer
Release Date: 2015-02-14
This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself. Industrial applications can use the proposed technique to improve their predictions.
Intelligent and Fuzzy Systems
This book presents recent research in intelligent and fuzzy techniques on Intelligent Industrial Informatics and Efficient Networks. This cutting-edge field integrates advanced technologies, such as artificial intelligence, machine learning and data analytics, into industrial processes, revolutionizing the way industries operate. The book presents the examples of the implementation of smart sensors and IoT devices, which facilitate real-time data collection and communication. High-speed, low-latency networks ensure that information flows effortlessly between devices, enabling timely responses and enabling the coordination of complex manufacturing processes. This network architecture supports the integration of edge computing, where data processing occurs closer to the source, reducing latency and enabling faster decision-making. The readers can benefit from this book for maintaining a leadership position among competitors in both manufacturing and service companies. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc. and Ph.D. students studying intelligent and fuzzy techniques. The book covers fuzzy logic theory and applications, heuristics and metaheuristics from optimization to machine learning, from quality management to risk management, making the book an excellent source for researchers.