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Author: Sai Abhishek Devar Publisher: ISBN: Category : Languages : en Pages : 42
Book Description
In this research work, we have implemented machine learning & deep-learning algorithms on realtime multivariate time series datasets in the manufacturing & health care fields. The research work is organized into two case-studies. The case study-1 is about rare event classification in multivariate time series in a pulp and paper manufacturing industry, data was collected of multiple sensors at each stage of the production line, the data contains a rare event of paper break that commonly occurs in the industry. For preprocessing we have implemented a sliding window approach for calculating the first-order difference method to capture the variation in the data over time. The sliding window approach helps to arrange the data for early prediction, for instance, we can set sliding window parameters to predict two or four minutes early as required. Our results indicate that for case study-1 best accuracy score was produced by the TensorFlow deep neural network model it was able to predict 50% of failures and 99% of non-failures with an overall accuracy of 75%. In case study-2 we have brain EEG signal data of patients which were collected with the help of the Stereo EEG Implantation strategy to measure their ability to remember words shown to him/her after distracting him /her with math problems and other activities. The data was collected at a health-care lab at UT-Southwestern Medical Center. The brain EEG signal data collected by the company was preprocessed by using Pearson's and Spearman's correlations, extracting bandwidth frequencies and basic statistics from EEG signal data extracted for each event, event in case study 2 refers to a word shown to a patient. We have used minimum redundancy and maximum relevance feature selection method for dimensionality reduction of the data and to get the most effective features out of all. For case-study 2 best results were produced by SVM-RBF i.e. 73% accuracy to predict if a patient will remember or not remember a word.
Author: Sai Abhishek Devar Publisher: ISBN: Category : Languages : en Pages : 42
Book Description
In this research work, we have implemented machine learning & deep-learning algorithms on realtime multivariate time series datasets in the manufacturing & health care fields. The research work is organized into two case-studies. The case study-1 is about rare event classification in multivariate time series in a pulp and paper manufacturing industry, data was collected of multiple sensors at each stage of the production line, the data contains a rare event of paper break that commonly occurs in the industry. For preprocessing we have implemented a sliding window approach for calculating the first-order difference method to capture the variation in the data over time. The sliding window approach helps to arrange the data for early prediction, for instance, we can set sliding window parameters to predict two or four minutes early as required. Our results indicate that for case study-1 best accuracy score was produced by the TensorFlow deep neural network model it was able to predict 50% of failures and 99% of non-failures with an overall accuracy of 75%. In case study-2 we have brain EEG signal data of patients which were collected with the help of the Stereo EEG Implantation strategy to measure their ability to remember words shown to him/her after distracting him /her with math problems and other activities. The data was collected at a health-care lab at UT-Southwestern Medical Center. The brain EEG signal data collected by the company was preprocessed by using Pearson's and Spearman's correlations, extracting bandwidth frequencies and basic statistics from EEG signal data extracted for each event, event in case study 2 refers to a word shown to a patient. We have used minimum redundancy and maximum relevance feature selection method for dimensionality reduction of the data and to get the most effective features out of all. For case-study 2 best results were produced by SVM-RBF i.e. 73% accuracy to predict if a patient will remember or not remember a word.
Author: Jason Brownlee Publisher: Machine Learning Mastery ISBN: Category : Computers Languages : en Pages : 572
Book Description
Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.
Author: Chun-Kit Ngan Publisher: BoD – Books on Demand ISBN: 1789847788 Category : Mathematics Languages : en Pages : 131
Book Description
This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.
Author: Christopher M. Bishop Publisher: Springer ISBN: 9781493938438 Category : Computers Languages : en Pages : 0
Book Description
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Author: Jonathan D. Cryer Publisher: Springer Science & Business Media ISBN: 0387759581 Category : Business & Economics Languages : en Pages : 501
Book Description
This book presents an accessible approach to understanding time series models and their applications. The ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment.
Author: Michael Botsch Publisher: Cuvillier Verlag ISBN: 3736968132 Category : Science Languages : en Pages : 217
Book Description
Classification of time series is an important task in various fields, e.g., medicine, finance, and industrial applications. This work discusses strong temporal classification using machine learning techniques. Here, two problems must be solved: the detection of those time instances when the class labels change and the correct assignment of the labels. For this purpose the scenario-based random forest algorithm and a segment and label approach are introduced. The latter is realized with either the augmented dynamic time warping similarity measure or with interpretable generalized radial basis function classifiers. The main application presented in this work is the detection and categorization of car crashes using machine learning. Depending on the crash severity different safety systems, e.g., belt tensioners or airbags must be deployed at time instances when the best-possible protection of passengers is assured.
Author: Richard O. Duda Publisher: John Wiley & Sons ISBN: 111858600X Category : Technology & Engineering Languages : en Pages : 680
Book Description
The first edition, published in 1973, has become a classicreference in the field. Now with the second edition, readers willfind information on key new topics such as neural networks andstatistical pattern recognition, the theory of machine learning,and the theory of invariances. Also included are worked examples,comparisons between different methods, extensive graphics, expandedexercises and computer project topics. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.
Author: Erick Giovani Sperandio Nascimento Publisher: ISBN: Category : Electronic books Languages : en Pages : 0
Book Description
The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data.
Author: Paul S.P. Cowpertwait Publisher: Springer Science & Business Media ISBN: 0387886982 Category : Mathematics Languages : en Pages : 262
Book Description
This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.
Author: Sai Abhishek Devar
Author: Sai Abhishek Devar
Author: Jason Brownlee
Author: Chun-Kit Ngan
Author: Christopher M. Bishop
Author: K.C. Fu
Author: Jonathan D. Cryer
Author: Michael Botsch
Author: Richard O. Duda
Author: Erick Giovani Sperandio Nascimento
Author: Paul S.P. Cowpertwait