Download or read book Extracting Knowledge From Time Series written by Boris P. Bezruchko and published by Springer Science & Business Media. This book was released on 2010-09-03 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.

Download or read book Hands On Time Series Analysis with R written by Rami Krispin and published by Packt Publishing Ltd. This book was released on 2019-05-31 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build efficient forecasting models using traditional time series models and machine learning algorithms. Key FeaturesPerform time series analysis and forecasting using R packages such as Forecast and h2oDevelop models and find patterns to create visualizations using the TSstudio and plotly packagesMaster statistics and implement time-series methods using examples mentionedBook Description Time series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods. What you will learnVisualize time series data and derive better insightsExplore auto-correlation and master statistical techniquesUse time series analysis tools from the stats, TSstudio, and forecast packagesExplore and identify seasonal and correlation patternsWork with different time series formats in RExplore time series models such as ARIMA, Holt-Winters, and moreEvaluate high-performance forecasting solutionsWho this book is for Hands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory.

Download or read book Practical Time Series Forecasting with R written by Galit Shmueli and published by Axelrod Schnall Publishers. This book was released on 2024-02-24 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Time Series Forecasting with R: A Hands-On Guide, Third Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source R software to develop effective forecasting solutions that extract business value from time series data. This edition features the R fable package, full color, enhanced organization, and new material. It includes: Popular forecasting methods including smoothing algorithms, regression models, ARIMA, neural networks, deep learning, and ensembles - A practical approach to evaluating the performance of forecasting solutions - A business-analytics exposition focused on linking time-series forecasting to business goals - Guided cases for integrating the acquired knowledge using real data - End-of-chapter problems to facilitate active learning - Data, R code, and instructor materials on companion website - Affordable and globally-available textbook, available in hardcover, paperback, and Kindle formats Practical Time Series Forecasting with R: A Hands-On Guide, Third Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, information systems, finance, and management.

Download or read book Practical Time Series Analysis written by Dr. Avishek Pal and published by Packt Publishing Ltd. This book was released on 2017-09-28 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.

Download or read book Machine Learning for Time Series Forecasting with Python written by Francesca Lazzeri and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Download or read book Practical Time Series Forecasting with R written by Galit Shmueli and published by Axelrod Schnall Publishers. This book was released on 2016-07-19 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source R software to develop effective forecasting solutions that extract business value from time-series data. Featuring improved organization and new material, the Second Edition also includes: - Popular forecasting methods including smoothing algorithms, regression models, and neural networks - A practical approach to evaluating the performance of forecasting solutions - A business-analytics exposition focused on linking time-series forecasting to business goals - Guided cases for integrating the acquired knowledge using real data* End-of-chapter problems to facilitate active learning - A companion site with data sets, R code, learning resources, and instructor materials (solutions to exercises, case studies) - Globally-available textbook, available in both softcover and Kindle formats Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, finance and management. For more information, visit forecastingbook.com

Download or read book Knowledge Discovery from Data Streams written by Joao Gama and published by CRC Press. This book was released on 2010-05-25 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents

Download or read book Feature Extraction and Classification on Time Series written by Miguel Méndez Pérez and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Time is involved in almost every scienti c eld one can think on. Observations of a phenomena are collected with the aim of study or explain its behavior. This collections lead to organized data called time series. Data mining community has spent a reasonable amount of time studying time series, in order to extract all meaningful knowledge from them. Humans are generally good comparing time series, but still, our capabilities are not scalable and we need to design algorithms and techniques that allow us to deal with high dimensional data and other problems. In this work we will focus in a speci c problem, extracting valid features of unlabeled time series obtained from aircraft sensors. These must serve as a summary of a ight and they also must include relevant details that serve to characterize it. This information will be used to feed an algorithm which can learn to classify ights in groups, reducing the number of necessary labeled data to obtain the desired accuracy using an active learning approach.

Download or read book Progress in Artificial Intelligence Knowledge Extraction Multi agent Systems Logic Programming and Constraint Solving written by Pavel Brazdil and published by Springer Science & Business Media. This book was released on 2001-12-05 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th Portuguese Conference on Artificial Intelligence, EPTA 2001, held in Porto, Portugal, in December 2001. The 21 revised long papers and 18 revised short papers were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections on extraction of knowledge from databases, AI techniques for financial time series analysis, multi-agent systems, AI logics and logic programming, constraint satisfaction, and AI planning.

Download or read book Machine Learning and Knowledge Extraction written by Andreas Holzinger and published by Springer Nature. This book was released on 2020-08-19 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, held in Dublin, Ireland, in August 2020. The 30 revised full papers presented were carefully reviewed and selected from 140 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity. Due to the Corona pandemic CD-MAKE 2020 was held as a virtual event.

Download or read book Extracting Knowledge From Opinion Mining written by Agrawal, Rashmi and published by IGI Global. This book was released on 2018-09-07 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining techniques are commonly used to extract meaningful information from the web, such as data from web documents, website usage logs, and hyperlinks. Building on this, modern organizations are focusing on running and improving their business methods and returns by using opinion mining. Extracting Knowledge From Opinion Mining is an essential resource that presents detailed information on web mining, business intelligence through opinion mining, and how to effectively use knowledge retrieved through mining operations. While highlighting relevant topics, including the differences between ontology-based opinion mining and feature-based opinion mining, this book is an ideal reference source for information technology professionals within research or business settings, graduate and post-graduate students, as well as scholars.

Download or read book Instance Selection and Construction for Data Mining written by Huan Liu and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.

Download or read book Connectionist Models written by David S. Touretzky and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connectionist Models contains the proceedings of the 1990 Connectionist Models Summer School held at the University of California at San Diego. The summer school provided a forum for students and faculty to assess the state of the art with regards to connectionist modeling. Topics covered range from theoretical analysis of networks to empirical investigations of learning algorithms; speech and image processing; cognitive psychology; computational neuroscience; and VLSI design. Comprised of 40 chapters, this book begins with an introduction to mean field, Boltzmann, and Hopfield networks, focusing on deterministic Boltzmann learning in networks with asymmetric connectivity; contrastive Hebbian learning in the continuous Hopfield model; and energy minimization and the satisfiability of propositional logic. Mean field networks that learn to discriminate temporally distorted strings are described. The next sections are devoted to reinforcement learning and genetic learning, along with temporal processing and modularity. Cognitive modeling and symbol processing as well as VLSI implementation are also discussed. This monograph will be of interest to both students and academicians concerned with connectionist modeling.

Download or read book Artificial Intelligence XXXVI written by Max Bramer and published by Springer Nature. This book was released on 2019-12-09 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2019, held in Cambridge, UK, in December 2019. The 29 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 49 submissions. The volume includes technical papers presenting new and innovative developments in the field as well as application papers presenting innovative applications of AI techniques in a number of subject domains. The papers are organized in the following topical sections: machine learning; knowledge discovery and data mining; agents, knowledge acquisition and ontologies; medical applications; applications of evolutionary algorithms; machine learning for time series data; applications of machine learning; and knowledge acquisition.

Download or read book New Frontiers in Computational Intelligence and Its Applications written by Masoud Mohammadian and published by IOS Press. This book was released on 2000 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence is a broad and active research area that is growing rapidly due to the many successful applications of these new techniques in very diverse problems. Many industries have benefited from adopting this technology. The increased number of patents and diverse range of products developed using computational intelligence methods is evidence of this fact. The goal of this book is to provide highlights of the current research in computational intelligence area. The book consists of research papers in the fields of neural networks, fuzzy logic, evolutionary computing, hybrid evolutionary computing-fuzzy logic systems, hybrid neural networks-evolutionary computing and fuzzy logic systems, image processing and vision, advances in robotics, control and manufacturing, and rough sets.

Download or read book Post Mining of Association Rules Techniques for Effective Knowledge Extraction written by Zhao, Yanchang and published by IGI Global. This book was released on 2009-05-31 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a systematic collection on post-mining, summarization and presentation of association rules, and new forms of association rules.

Download or read book Human Interface and the Management of Information Information and Knowledge Design and Evaluation written by Sakae Yamamoto and published by Springer. This book was released on 2014-06-07 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 8521 and 8522 constitutes the refereed proceedings of the Human Interface and the Management of Information thematic track, held as part of the 16th International Conference on Human-Computer Interaction, HCII 2014, held in Heraklion, Greece, in June 2014, jointly with 13 other thematically similar conferences. The total of 1476 papers and 220 posters presented at the HCII 2014 conferences were carefully reviewed and selected from 4766 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. This volume contains papers addressing the following major topics: visualization methods and techniques; multimodal interaction; knowledge management; information search and retrieval; supporting collaboration; design and evaluation methods and studies.