Download or read book Neural Networks and Sea Time Series written by Brunello Tirozzi and published by Springer Science & Business Media. This book was released on 2007-10-12 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Devoted to the application of neural networks to the concrete problem of time series of sea data Good reference for a diverse audience of grad students, researchers, and practitioners in applied mathematics, data analysis, meteorlogy, hydraulic, civil and marine engineering Methods, models and alogrithms developed in the work are useful for the construction of sea structures, ports, and marine experiments

Download or read book Neural Networks and Sea Time Series written by Brunello Tirozzi and published by Birkhäuser. This book was released on 2005-11-21 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Devoted to the application of neural networks to the concrete problem of time series of sea data Good reference for a diverse audience of grad students, researchers, and practitioners in applied mathematics, data analysis, meteorlogy, hydraulic, civil and marine engineering Methods, models and alogrithms developed in the work are useful for the construction of sea structures, ports, and marine experiments

Download or read book Time Series Forecasting using Deep Learning written by Ivan Gridin and published by BPB Publications. This book was released on 2021-10-15 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks KEY FEATURES ● Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts. ● Includes practical demonstration of robust deep learning prediction models with exciting use-cases. ● Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence. DESCRIPTION This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch. The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task. Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques. WHAT YOU WILL LEARN ● Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics. ● Learn the basics of neural architecture search with Neural Network Intelligence. ● Combine standard statistical analysis methods with deep learning approaches. ● Automate the search for optimal predictive architecture. ● Design your custom neural network architecture for specific tasks. ● Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes. WHO THIS BOOK IS FOR This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed. TABLE OF CONTENTS 1. Time Series Problems and Challenges 2. Deep Learning with PyTorch 3. Time Series as Deep Learning Problem 4. Recurrent Neural Networks 5. Advanced Forecasting Models 6. PyTorch Model Tuning with Neural Network Intelligence 7. Applying Deep Learning to Real-world Forecasting Problems 8. PyTorch Forecasting Package 9. What is Next?

Download or read book Sea surface Temperature Estimation written by C. J. Van Vliet and published by . This book was released on 1967 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: An autocorrelation analysis of six temperature records from the North Pacific and North Atlantic up to 40 years in length showed the existence of an oscillatory function with period 1 year for all the stations studied, and of another oscillatory function with period 0.5 year for most of the stations. A regression model containing annual and semiannual oscillatory terms was found to provide a good statistical fit to the observed daily temperatures. No long-term trends were detected in the sequences of annual mean temperatures, but there were significant differences among these temperatures. (Author).

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.

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

Download or read book Comparison of Sea Surface Temperature Modelling Between Statistical and Neural Network Methods written by Peiyuan Huang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Spatial-temporal patterns are spatially orientated time series that arise in many natural contexts such as atmospheric science. There are various existing spatial-temporal pattern forecasting models from both statistics and machine learning. Our work isaimed at comparing statistical methods with machine learning approaches regarding covariate-free spatial-temporal pattern forecasting tasks, using sea surface temperature data, with the objective of comparing their forecasting performances in terms ofboth point forecasts and forecast intervals. Although it is commonly believed that deep learning models such as convolutional long short term memory models can outperform statistical frameworks in forecasting tasks, we found that in our dataset deep learning approach is not necessarily superior, although performance did improve if dropout was deployed. Furthermore, we found that a simplified neural network model, the echo-state network, also exhibited superior performance compared to deep learning models"--

Download or read book Time Series Analysis Using Neural Networks written by Ritu Vijay and published by LAP Lambert Academic Publishing. This book was released on 2012-08 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks are suitable for many tasks in pattern recognition and machine learning. Unlike conventional techniques for time series analysis, an artificial neural network needs little information about the time series data and can be applied to a broad range of problems. The usage of artificial neural networks for time series analysis relies purely on the data that were observed. As Radial Basis networks with one hidden layer is capable of approximating any measurable function. An artificial neural network is powerful enough to represent any form of time series. The capability to generalize allows artificial neural networks to learn even in the case of noisy and/or missing data. Another advantage over linear models is the network's ability to represent nonlinear time series. Prediction of tides is very much essential for human activities and to reduce the construction cost in marine environment. This book presents an application of the artificial neural network with Radial basis function for accurate prediction of tides. This neural network model predicts the time series data of hourly tides directly while using an an efficient learning process.

Download or read book Neural Computing written by Philip D. Wasserman and published by Van Nostrand Reinhold Company. This book was released on 1989 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book for nonspecialists clearly explains major algorithms and demystifies the rigorous math involved in neural networks. Uses a step-by-step approach for implementing commonly used paradigms.

Download or read book Using Artificial Neural Networks for Timeseries Smoothing and Forecasting written by Jaromír Vrbka and published by Springer Nature. This book was released on 2021-09-04 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.

Download or read book Complex Intelligent and Software Intensive Systems written by Leonard Barolli and published by Springer Nature. This book was released on 2021-06-29 with total page 761 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the proceedings of the 15th International Conference on Complex, Intelligent, and Software Intensive Systems, which took place in Asan, Korea, on July 1–3, 2021. Software intensive systems are systems, which heavily interact with other systems, sensors, actuators, devices, and other software systems and users. More and more domains are involved with software intensive systems, e.g., automotive, telecommunication systems, embedded systems in general, industrial automation systems, and business applications. Moreover, the outcome of web services delivers a new platform for enabling software intensive systems. Complex systems research is focused on the overall understanding of systems rather than its components. Complex systems are very much characterized by the changing environments in which they act by their multiple internal and external interactions. They evolve and adapt through internal and external dynamic interactions. The development of intelligent systems and agents, which is each time more characterized by the use of ontologies and their logical foundations build a fruitful impulse for both software intensive systems and complex systems. Recent research in the field of intelligent systems, robotics, neuroscience, artificial intelligence, and cognitive sciences is very important factor for the future development and innovation of software intensive and complex systems. The aim of the book is to deliver a platform of scientific interaction between the three interwoven challenging areas of research and development of future ICT-enabled applications: Software intensive systems, complex systems, and intelligent systems.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of the Fourth International Conference in Ocean Engineering ICOE2018 written by K. Murali and published by Springer. This book was released on 2018-12-31 with total page 929 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises selected proceedings of the Fourth International Conference in Ocean Engineering (ICOE2018), focusing on emerging opportunities and challenges in the field of ocean engineering and offshore structures. It includes state-of-the-art content from leading international experts, making it a valuable resource for researchers and practicing engineers alike.

Download or read book Singular Spectrum Analysis with R written by Nina Golyandina and published by Springer. This book was released on 2018-06-14 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA). SSA is a well-known methodology for the analysis and forecasting of time series. Since quite recently, SSA is also being used to analyze digital images and other objects that are not necessarily of planar or rectangular form and may contain gaps. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas, most notably those associated with time series and digital images. An effective, comfortable and accessible implementation of SSA is provided by the R-package Rssa, which is available from CRAN and reviewed in this book. Written by prominent statisticians who have extensive experience with SSA, the book (a) presents the up-to-date SSA methodology, including multidimensional extensions, in language accessible to a large circle of users, (b) combines different versions of SSA into a single tool, (c) shows the diverse tasks that SSA can be used for, (d) formally describes the main SSA methods and algorithms, and (e) provides tutorials on the Rssa package and the use of SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The book is written on a level accessible to a broad audience and includes a wealth of examples; hence it can also be used as a textbook for undergraduate and postgraduate courses on time series analysis and signal processing.

Download or read book A Primer on Machine Learning Applications in Civil Engineering written by Paresh Chandra Deka and published by CRC Press. This book was released on 2019-10-28 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises

Download or read book Communications Signal Processing and Systems written by Qilian Liang and published by Springer. This book was released on 2019-08-14 with total page 1462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together papers from the 2018 International Conference on Communications, Signal Processing, and Systems, which was held in Dalian, China on July 14–16, 2018. Presenting the latest developments and discussing the interactions and links between these multidisciplinary fields, the book spans topics ranging from communications, signal processing and systems. It is aimed at undergraduate and graduate electrical engineering, computer science and mathematics students, researchers and engineers from academia and industry as well as government employees.

Download or read book Coastal Processes written by C. A. Brebbia and published by WIT Press. This book was released on 2009 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this conference is to provide a forum for the dissemination and exchange of scientific and technical advancing international knowledge transfer ideas and progress among researchers concerned with the study of physical processes operating at the coast.