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Author : Morten With Pedersen
Publisher :
ISBN 13 :
Total Pages : 322 pages
Book Rating : 4.:/5 (46 download)
Download or read book Optimization of Recurrent Neural Networks for Time Series Modeling written by Morten With Pedersen and published by . This book was released on 1997 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 572 pages
Book Rating : 4./5 ( download)
Download or read book Deep Learning for Time Series Forecasting written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-08-30 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 245 pages
Book Rating : 4./5 ( download)
Download or read book Long Short-Term Memory Networks With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2017-07-20 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. In this laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about LSTMs. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of the method on your sequence prediction problems.
Author : Filippo Maria Bianchi
Publisher : Springer
ISBN 13 : 3319703382
Total Pages : 74 pages
Book Rating : 4.3/5 (197 download)
Download or read book Recurrent Neural Networks for Short-Term Load Forecasting written by Filippo Maria Bianchi and published by Springer. This book was released on 2017-11-09 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
ISBN 13 :
Total Pages : 283 pages
Book Rating : 4./5 ( download)
Download or read book TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Deep Leraning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, timeseries forecasting, and dynamic system modeling and control. Dynamic neural networks are good at timeseries prediction. You can use the Neural Net Time Series app to solve different kinds of time series problems It is generally best to start with the GUI, and then to use the GUI to automatically generate command line scripts. Before using either method, the first step is to define the problem by selecting a data set. Each GUI has access to many sample data sets that you can use to experiment with the toolbox. If you have a specific problem that you want to solve, you can load your own data into the workspace. With MATLAB is possibe to solve three different kinds of time series problems. In the first type of time series problem, you would like to predict future values of a time series y(t) from past values of that time series and past values of a second time series x(t). This form of prediction is called nonlinear autoregressive network with exogenous (external) input, or NARX. In the second type of time series problem, there is only one series involved. The future values of a time series y(t) are predicted only from past values of that series. This form of prediction is called nonlinear autoregressive, or NAR. The third time series problem is similar to the first type, in that two series are involved, an input series (predictors) x(t) and an output series (responses) y(t). Here you want to predict values of y(t) from previous values of x(t), but without knowledge of previous values of y(t). This book develops methods for time series forecasting using neural networks across MATLAB
Author : Francois Chollet
Publisher : Simon and Schuster
ISBN 13 : 1638352046
Total Pages : 597 pages
Book Rating : 4.6/5 (383 download)
Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Author : Rob J Hyndman
Publisher : OTexts
ISBN 13 : 0987507117
Total Pages : 380 pages
Book Rating : 4.9/5 (875 download)
Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author : Rajdeep
Publisher : BPB Publications
ISBN 13 : 9391392229
Total Pages : 353 pages
Book Rating : 4.3/5 (913 download)
Download or read book Mastering TensorFlow 2.x written by Rajdeep and published by BPB Publications. This book was released on 2022-03-24 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work with TensorFlow and Keras for real performance of deep learning KEY FEATURES ● Combines theory and implementation with in-detail use-cases. ● Coverage on both, TensorFlow 1.x and 2.x with elaborated concepts. ● Exposure to Distributed Training, GANs and Reinforcement Learning. DESCRIPTION Mastering TensorFlow 2.x is a must to read and practice if you are interested in building various kinds of neural networks with high level TensorFlow and Keras APIs. The book begins with the basics of TensorFlow and neural network concepts, and goes into specific topics like image classification, object detection, time series forecasting and Generative Adversarial Networks. While we are practicing TensorFlow 2.6 in this book, the version of Tensorflow will change with time; however you can still use this book to witness how Tensorflow outperforms. This book includes the use of a local Jupyter notebook and the use of Google Colab in various use cases including GAN and Image classification tasks. While you explore the performance of TensorFlow, the book also covers various concepts and in-detail explanations around reinforcement learning, model optimization and time series models. WHAT YOU WILL LEARN ● Getting started with Tensorflow 2.x and basic building blocks. ● Get well versed in functional programming with TensorFlow. ● Practice Time Series analysis along with strong understanding of concepts. ● Get introduced to use of TensorFlow in Reinforcement learning and Generative Adversarial Networks. ● Train distributed models and how to optimize them. WHO THIS BOOK IS FOR This book is designed for machine learning engineers, NLP engineers and deep learning practitioners who want to utilize the performance of TensorFlow in their ML and AI projects. Readers are expected to have some familiarity with Tensorflow and the basics of machine learning would be helpful. TABLE OF CONTENTS 1. Getting started with TensorFlow 2.x 2. Machine Learning with TensorFlow 2.x 3. Keras based APIs 4. Convolutional Neural Networks in Tensorflow 5. Text Processing with TensorFlow 2.x 6. Time Series Forecasting with TensorFlow 2.x 7. Distributed Training and DataInput pipelines 8. Reinforcement Learning 9. Model Optimization 10. Generative Adversarial Networks
Author : Zhang, Ming
Publisher : IGI Global
ISBN 13 : 1466621761
Total Pages : 455 pages
Book Rating : 4.4/5 (666 download)
Download or read book Artificial Higher Order Neural Networks for Modeling and Simulation written by Zhang, Ming and published by IGI Global. This book was released on 2012-10-31 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.
Author : Rob Hyndman
Publisher : Springer Science & Business Media
ISBN 13 : 3540719180
Total Pages : 362 pages
Book Rating : 4.5/5 (47 download)
Download or read book Forecasting with Exponential Smoothing written by Rob Hyndman and published by Springer Science & Business Media. This book was released on 2008-06-19 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.
Author : Chris Chatfield
Publisher : CRC Press
ISBN 13 : 1420036203
Total Pages : 281 pages
Book Rating : 4.4/5 (2 download)
Download or read book Time-Series Forecasting written by Chris Chatfield and published by CRC Press. This book was released on 2000-10-25 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space
Author : Vincent Lemaire
Publisher : Springer Nature
ISBN 13 : 3030390985
Total Pages : 236 pages
Book Rating : 4.0/5 (33 download)
Download or read book Advanced Analytics and Learning on Temporal Data written by Vincent Lemaire and published by Springer Nature. This book was released on 2020-01-22 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.
Author : Larry Medsker
Publisher : CRC Press
ISBN 13 : 9781420049176
Total Pages : 414 pages
Book Rating : 4.0/5 (491 download)
Download or read book Recurrent Neural Networks written by Larry Medsker and published by CRC Press. This book was released on 1999-12-20 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks. This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects. The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.
Author : Carl Edward Rasmussen
Publisher : MIT Press
ISBN 13 : 026218253X
Total Pages : 266 pages
Book Rating : 4.2/5 (621 download)
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Author : Jesus Soto
Publisher : Springer
ISBN 13 : 3319712640
Total Pages : 103 pages
Book Rating : 4.3/5 (197 download)
Download or read book Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction written by Jesus Soto and published by Springer. This book was released on 2017-11-19 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.
Author : R. A. Aliev
Publisher :
ISBN 13 : 9789537619084
Total Pages : pages
Book Rating : 4.6/5 (19 download)
Download or read book Recurrent Fuzzy Neural Networks and Their Performance Analysis written by R. A. Aliev and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In spite of great importance of fuzzy neural networks for solving wide range of real-world problems, unfortunately, little progress has been made in their development. In this study we have discussed recurrent neural networks with fuzzy weights and biases as adjustable parameters and internal feedback loops, which allows capturing dynamic response of a system without using external feedback through delays. In this case all the nodes are able to process linguistic information. As the main problem regarding fuzzy and recurrent fuzzy neural networks that limits their application range is the difficulty of proper adjustment of fuzzy weights and biases, we put an emphasize on the RFNN training algorithm. We have proposed the standard DEO-based method for learning of recurrent fuzzy neural network. The optimization method, customized for RFNN training, compares favorably with the existing gradient-based error minimization method as it is less complex and is more likely to locate the global minimum of network error. As the method does not require derivative information, it is very effective in case when dealing with different distance functions. Also, the considered global optimization algorithm can provide high accuracy of fuzzy mapping with relatively smaller network size. The RFNN was tested on a number of benchmark identification and time-series forecasting problems well-known in the literature as well as on application problems. Experimental results demonstrated very good performance on all considered problems.
Author : Grzegorz Rozenberg
Publisher : Springer
ISBN 13 : 9783540929093
Total Pages : 2052 pages
Book Rating : 4.9/5 (29 download)
Download or read book Handbook of Natural Computing written by Grzegorz Rozenberg and published by Springer. This book was released on 2012-07-09 with total page 2052 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Computing is the field of research that investigates both human-designed computing inspired by nature and computing taking place in nature, i.e., it investigates models and computational techniques inspired by nature and also it investigates phenomena taking place in nature in terms of information processing. Examples of the first strand of research covered by the handbook include neural computation inspired by the functioning of the brain; evolutionary computation inspired by Darwinian evolution of species; cellular automata inspired by intercellular communication; swarm intelligence inspired by the behavior of groups of organisms; artificial immune systems inspired by the natural immune system; artificial life systems inspired by the properties of natural life in general; membrane computing inspired by the compartmentalized ways in which cells process information; and amorphous computing inspired by morphogenesis. Other examples of natural-computing paradigms are molecular computing and quantum computing, where the goal is to replace traditional electronic hardware, e.g., by bioware in molecular computing. In molecular computing, data are encoded as biomolecules and then molecular biology tools are used to transform the data, thus performing computations. In quantum computing, one exploits quantum-mechanical phenomena to perform computations and secure communications more efficiently than classical physics and, hence, traditional hardware allows. The second strand of research covered by the handbook, computation taking place in nature, is represented by investigations into, among others, the computational nature of self-assembly, which lies at the core of nanoscience, the computational nature of developmental processes, the computational nature of biochemical reactions, the computational nature of bacterial communication, the computational nature of brain processes, and the systems biology approach to bionetworks where cellular processes are treated in terms of communication and interaction, and, hence, in terms of computation. We are now witnessing exciting interaction between computer science and the natural sciences. While the natural sciences are rapidly absorbing notions, techniques and methodologies intrinsic to information processing, computer science is adapting and extending its traditional notion of computation, and computational techniques, to account for computation taking place in nature around us. Natural Computing is an important catalyst for this two-way interaction, and this handbook is a major record of this important development.
