Tag: Prediction

Deep Learning in Multi-step Prediction of Chaotic Dynamics From Deterministic Models to Real-World Systems


Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso
English | EPUB | 2022 | 111 Pages | ISBN : 3030944816 | 14.6 MB
The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.

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Practical I-Ching ancient Eastern prediction science to modern life application


Tuan Khuong, "Practical I-Ching: ancient Eastern prediction science to modern life application"
English | 2015 | ASIN: B016XPTIJO | EPUB | pages: 23 | 0.5 mb
This booklet focuses on practical aspect of application of I-ching – an ancient prediction science from the East – into all aspects of life, includes works, business, stock market prediction, lottery….

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Ionospheric Prediction and Forecasting


Ionospheric Prediction and Forecasting By Bruno Zolesi, Ljiljana R. Cander (auth.)
2014 | 240 Pages | ISBN: 3642384293 | PDF | 12 MB
This book describes how to predict and forecast the state of planet Earth’s ionosphere under quiet and disturbed conditions in terms of dynamical processes in the weakly ionized plasma media of the upper atmosphere and their relation to available modern measurements and modelling techniques. It explains the close relationship between the state of the media and the radio wave propagation conditions via this media. The prediction and forecasting algorithms, methods and models are oriented towards providing a practical approach to ionospherically dependent systems design and engineering. Proper understanding of the ionosphere is of fundamental practical importance because it is an essential part of telecommunication and navigation systems that use the ionosphere to function or would function much better in its nonappearance on the Earth and on any planet with an atmosphere.

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Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms


Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms
English | 2021 | ISBN: 0367615800 | 203 Pages | PDF True | 8 MB
This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering, biomedical engineering, and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work.

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