Thematische Bibliographien / Neural time series / Bücher
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Bücher zum Thema „Neural time series“

Autor: Grafiati
Veröffentlicht am 18. Mai 2024

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1

Vemuri, V. Artificial neural networks: Forecasting time series. Los Alamitos, Calif: IEEE Computer Society Press, 1994.

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2

Athanasios, Kehagias, Hrsg. Predictive modular neural networks: Applications to time series. Boston: Kluwer Academic Publishers, 1998.

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3

Neural, novel & hybrid algorithms for time series prediction. New York: John Wiley & Sons, 1995.

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4

Petridis, Vassilios. Predictive Modular Neural Networks: Applications to Time Series. Boston, MA: Springer US, 1998.

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5

Neural network time series forecasting of financial markets. Chichester: Wiley, 1994.

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6

Zanas, N. Artificial neural networks and time series models in economic forecasting. Manchester: UMIST, 1997.

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7

Brunello, Tirozzi, Hrsg. Neural networks and sea time series: Reconstruction and extreme event analysis. Boston, MA: Birkhäuser, 2005.

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8

1949-, Creedy John, und Martin Vance 1955-, Hrsg. Nonlinear economic models: Cross-sectional, time series and neural network applications. Cheltenham, U.K: E. Elgar, 1997.

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9

Roitman, Valéria de Lima. A utilização de redes neurais para previsão de séries temporais. Rio de Janeiro: IPEA, Diretoria de Estudos Macroeconômicos, 2001.

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10

Tetko, Igor V., Věra Kůrková, Pavel Karpov und Fabian Theis, Hrsg. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30490-4.

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11

Soto, Jesus, Patricia Melin und Oscar Castillo. Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71264-2.

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12

Künstliche neuronale Netze versus ökonometrische und zeitreihenanalytische Verfahren zur Prognose ökonomischer Zeitreihen. Frankfurt am Main: P. Lang, 1997.

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13

Cooper, Neil. Modelling and forecasting the recent sterling-deutschmark exchange rate: A comparison between modern time-series econometrics and artificial neural networks. [s.l.]: typescript, 1994.

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14

Intelligent systems and financial forecasting. London: Springer, 1997.

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15

Cohen, Mike X. Analyzing Neural Time Series Data. The MIT Press, 2014. http://dx.doi.org/10.7551/mitpress/9609.001.0001.

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16

Tirozzi, Brunello, Silvia Puca und Stefano Pittalis. Neural Networks and Sea Time Series. Springer, 2008.

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17

Neural Networks and Sea Time Series. Boston, MA: Birkhäuser Boston, 2006. http://dx.doi.org/10.1007/0-8176-4459-8.

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18

Cohen, Mike X. Analyzing Neural Time Series Data: Theory and Practice. MIT Press, 2014.

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19

Cohen, Mike X. Analyzing Neural Time Series Data: Theory and Practice. MIT Press, 2014.

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20

Cohen, Mike X. Analyzing Neural Time Series Data: Theory and Practice. MIT Press, 2014.

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21

Petridis, Vassilios, und Athanasios Kehagias. Predictive Modular Neural Networks: Applications to Time Series. Springer, 2011.

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22

Analyzing Neural Time Series Data: Theory and Practice. The MIT Press, 2014.

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23

Vemuri, V. Rao. Artificial Neural Networks: Forecasting Time Series/IEEE Catalog No Eh03822 (Forecasting Time Series/ieee Catalog No Eh03822). Institute of Electrical & Electronics Enginee, 1993.

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24

PEREZ, C. TIME SERIES ANALYSIS with NEURAL NETWORKS. EXAMPLES Across MATLAB. Independently Published, 2019.

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25

Current approaches in neural network modeling of financial time series. Bangalore: Indian Institute of Management, 2009.

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26

Tirozzi, Brunello, Silvia Puca, Stefano Pittalis, Antonello Bruschi und Sara Morucci. Neural Networks and Sea Time Series: Reconstruction and Extreme-Event Analysis. Springer London, Limited, 2007.

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27

Neural networks and sea time series: Reconstruction and extreme event analysis. Boston, MA: Birkhauser, 2006.

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28

Sarangapani, Jagannathan. Neural Network Control of Nonlinear Discrete-Time Systems. Control Engineering Series. Taylor & Francis Group, 2010.

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29

Deshpande, Rohit. Neural Network Approach for Rainfall Time Series Prediction: An Interactive Book. Independently Published, 2018.

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30

Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction. Springer, 2017.

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31

Shreve, Steven E., und Dimitri P. Bertsekas. Stochastic Optimal Control: The Discrete-Time Case (Optimization and Neural Computation Series). Athena Scientific, 2007.

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32

Lorentz, C. SUPERVISED LEARNING TECHNIQUES. TIME SERIES FORECASTING. EXAMPLES with NEURAL NETWORKS and MATLAB. Independently Published, 2020.

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33

Theis, Fabian, Věra Kůrková, Igor V. Tetko und Pavel Karpov. Artificial Neural Networks and Machine Learning – ICANN 2019 : Text and Time Series: 28th International Conference on Artificial Neural Networks, ... Springer, 2019.

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34

Tirozzi, Brunello, Silvia Puca, Stefano Pittalis, Antonello Bruschi, Sara Morucci, Enrico Ferraro und Stefano Corsini. Neural Networks and Sea Time Series: Reconstruction and Extreme-Event Analysis (Modeling and Simulation in Science, Engineering and Technology). Birkhäuser Boston, 2005.

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35

MARQUÉS, Felicidad. AUTOMATIC TIME SERIES FORECASTING Using NEURAL NETWORKS, STATE SPACE and ARIMAX MODELS. Examples with R. Independently Published, 2021.

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36

Pemodelan dan peramalan data di Indonesia dengan pendekatan ekonometrik time series dan neural network: Laporan penelitian. [Bandung]: Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Padjadjaran, 2007.

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37

Perez, C. Applications with Neural Networks: Fit Regression Models, Clustering, Pattern Recognition and Time Series Models. Examples with MATLAB. Independently Published, 2019.

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38

Whitenack, Daniel. Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language. Packt Publishing - ebooks Account, 2017.

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39

(Editor), John Creedy, und Vance Martin (Editor), Hrsg. Nonlinear Economic Models: Cross-Sectional, Times Series and Neural Network Applications. Edward Elgar Publishing, 1997.

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40

Walsh, Bruce, und Michael Lynch. The Neutral Divergence of Quantitative Traits. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0012.

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The joint action of genetic drift and mutation results in the divergence of trait means over time. This chapter examines the expected amount of divergence, which forms the basis for a number of tests on whether an observed pattern is either too large relative to drift (suggesting directional selection) or two small (suggesting stabilizing selection). It then applies these results to examine tests for selection over a very diverse range of data sets, ranging from a stratophenetic series of fossils to divergence in gene expression over time. It also examines a number of trait-augmented marked-based tests (such as using the QTLs or GWAS hits for a trait) for departures from neutrality.
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41

Silberstein, Michael, W. M. Stuckey und Timothy McDevitt. Conscious Experience and the Block Universe. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198807087.003.0008.

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Chapter 7 discusses the “argument from time as experienced,” which is alleged to be the most damning argument against the block universe. It shows that as typically presented the argument from experience is easily refuted. It then argues that all the “A-series” alternatives are, at the very least, no better off than the block universe at accounting for time as experienced. It further argues that the standard defense of the block universe in terms of the “argument from the equivalence of experience” is a good one. However, the proponent of the block universe, having eliminated the competitors, still has to explain time as experienced. Here it is argued that the standard moves, such as invoking some physical arrow of time or mental representations/neural mechanisms, fail. Indeed, it is argued that the block universe posit makes it doubly impossible to resolve the generation/hard problem of conscious experience, including time as experienced.
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42

DeFelipe, Javier. The Golden Era for Artistic Creativity in Neuroscience. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190842833.003.0003.

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Section B focuses on the artistic skills and emotions of Cajal and other important pioneers in neuroscience, using their own words to describe the artistic emotions they experienced when visualizing the neural elements. This section also deals with the verification in recent years of many of the descriptions and illustrations of the early neuroanatomists, particularly those of Cajal. The last part of this section describes how the neuronal forests have served as an unlimited source of artistic and poetic inspiration to many scientists, not only in the early days of the research of the brain, but also in modern times.
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43

Anderson, James A. An Engineer’s Introduction to Neuroscience. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0006.

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When building something, it is essential to know the hardware. This chapter contains key things to know about the active components of the brain: nerve cells (aka neurons). Neurons have severe performance limitations. Problems include high energy consumption, mechanical and physiological sensitivity, unreliability, limited connectivity, and difficulty in wiring neurons together. Neurons are at least a million times slower to “compute” than a modern electronic device. This slow speed cannot be avoided because the neuron has to deal with high electrical capacity and resistance and slow conduction times to move information from neuron to neuron. A specialization called the action potential serves as a long-distance communications mechanism. However, the neuron also has major virtues including the ability to integrate, communicate, and process information from multiple sources, and it acts like a tiny electrochemical analog computer.
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44

Warner, Kristen J. ABC’s Scandal and Black Women’s Fandom. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252039577.003.0003.

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This chapter analyzes the ways Black women fans have reacted to not being visible in the dominant spaces of fandom. It offers the fandom of ABC's Scandal—a program with Black women in central positions both behind and in front of the camera—as a primary example of the ways Black women fan communities work toward reinscription. Premiering on ABC in April 2012, the prime-time dramatic series produced by Grey's Anatomy showrunner Shonda Rhimes and starring African American actress Kerry Washington became a literal embodiment of the type of labor Black women practice in fan spaces. In this case, Black female fans have transformed the central Black lead, canonically drawn as normative and racially neutral, into a culturally specific Black character. This is commonplace labor for non-White fans—particularly in the early-twenty-first-century's so-called postrace moment.
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45

Robbins, Trevor. Impulsivity and Drug Addiction: A Neurobiological Perspective. Herausgegeben von Jon E. Grant und Marc N. Potenza. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195389715.013.0078.

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A conceptual analysis of the impulsivity construct in behavioral and neurobiological terms is followed by an analysis of its causal role in certain forms of drug addiction in both human and animal studies. The main focus of this chapter is on a rat model of impulsivity based on premature responding in the five-choice serial reaction time task and a more detailed characterization of this phenotype in neurobehavioral, neurochemical, and genetic terms. Evidence is surveyed that high impulsivity on this task is associated with the escalation subsequently of cocaine self-administration behavior and also with a tendency toward compulsive cocaine seeking. Novelty reactivity, by contrast, is associated with the enhanced acquisition of self-administration, but not with the escalation of intravenous self-administration of cocaine or the development of compulsive behavior associated with cocaine seeking. These results indicate that the vulnerability to stimulant addiction may depend on different factors, as expressed through distinct presumed endophenotypes. These observations help us further to dissociate various aspects of the impulsivity construct in neural as well as behavioral terms.
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46

Straub, Rainer H. Neuroendocrine system. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0022.

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Endocrine abnormalities are very common in patients with chronic autoimmune rheumatic diseases (CARDs) due to the systemic involvement of the central nervous system and endocrine glands. In recent years, the response of the endocrine (and also neuronal) system to peripheral inflammation has been linked to overall energy regulation of the diseased body and bioenergetics of immune cells. In CARDs, hormonal and neuronal pathways are outstandingly important in partitioning energy-rich fuels from muscle, brain, and fat tissue to the activated immune system. Neuroendocrine regulation of fuel allocation has been positively selected as an adaptive programme for transient serious, albeit non-life-threatening, inflammatory episodes. In CARDs, mistakenly, the adaptive programmes are used again but for a much longer time leading to systemic disease sequelae with endocrine (and also neuronal) abnormalities. The major endocrine alterations are depicted in the following list: mild activation of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, inadequate secretion of ACTH and cortisol relative to inflammation, loss of androgens, inhibition of the hypothalamic-pituitary-gonadal axis and fertility problems, high serum levels of oestrogens relative to androgens, fat deposits adjacent to inflamed tissue, increase of serum prolactin, and hyperinsulinaemia (and the metabolic syndrome). Neuroendocrine abnormalities are demonstrated using this framework that can explain many CARD-related endocrine disturbances. This chapter gives an overview on pathophysiology of neuroendocrine alterations in the context of energy regulation.
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47

Straub, Rainer H. Neuroendocrine system. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199642489.003.0022_update_002.

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Endocrine abnormalities are very common in patients with chronic autoimmune rheumatic diseases (CARDs) due to the systemic involvement of the central nervous system and endocrine glands. In recent years, the response of the endocrine (and also neuronal) system to peripheral inflammation has been linked to overall energy regulation of the diseased body and bioenergetics of immune cells. In CARDs, hormonal and neuronal pathways are outstandingly important in partitioning energy-rich fuels from muscle, brain, and fat tissue to the activated immune system. Neuroendocrine regulation of fuel allocation has been positively selected as an adaptive programme for transient serious, albeit non-life-threatening, inflammatory episodes. In CARDs, mistakenly, the adaptive programmes are used again but for a much longer time leading to systemic disease sequelae with endocrine (and also neuronal) abnormalities. The major endocrine alterations are depicted in the following list: mild activation of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, inadequate secretion of ACTH and cortisol relative to inflammation, loss of androgens, inhibition of the hypothalamic-pituitary-gonadal axis and fertility problems, high serum levels of oestrogens relative to androgens, fat deposits adjacent to inflamed tissue, increase of serum prolactin, and hyperinsulinaemia (and the metabolic syndrome). Neuroendocrine abnormalities are demonstrated using this framework that can explain many CARD-related endocrine disturbances. This chapter gives an overview on pathophysiology of neuroendocrine alterations in the context of energy regulation.
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48

Straub, Rainer H. Neuroendocrine system. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199642489.003.0022_update_003.

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Endocrine abnormalities are very common in patients with chronic autoimmune rheumatic diseases (CARDs) due to the systemic involvement of the central nervous system and endocrine glands. In recent years, the response of the endocrine (and also neuronal) system to peripheral inflammation has been linked to overall energy regulation of the diseased body and bioenergetics of immune cells. In CARDs, hormonal and neuronal pathways are outstandingly important in partitioning energy-rich fuels from muscle, brain, and fat tissue to the activated immune system. Neuroendocrine regulation of fuel allocation has been positively selected as an adaptive programme for transient serious, albeit non-life-threatening, inflammatory episodes. In CARDs, mistakenly, the adaptive programmes are used again but for a much longer time leading to systemic disease sequelae with endocrine (and also neuronal) abnormalities. The major endocrine alterations are depicted in the following list: mild activation of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, inadequate secretion of ACTH and cortisol relative to inflammation, loss of androgens, inhibition of the hypothalamic-pituitary-gonadal axis and fertility problems, high serum levels of oestrogens relative to androgens, fat deposits adjacent to inflamed tissue, increase of serum prolactin, and hyperinsulinaemia (and the metabolic syndrome). Neuroendocrine abnormalities are demonstrated using this framework that can explain many CARD-related endocrine disturbances. This chapter gives an overview on pathophysiology of neuroendocrine alterations in the context of energy regulation.
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49

Verstraten, Frans A. J., und Peter J. Bex. The Motion Aftereffect. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0082.

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The aftereffect of motion is one of the oldest known illusions. It refers to the illusory motion of a stationary scene after some time of adaptation to real motion. While it is still unknown whether this adaptation effect has any functional value, it surely has served well as a tool to investigate the functional organization of the visual system. In this chapter some of the classic findings are discussed. More recent work using complex stimuli, attentional modulation, higher order motion, as well as modern neuro-imaging techniques has provided vision scientists with surprising new insights. Discussion of the related concepts of motion perception, motion transparency, and interocular transfer are included.
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50

Gage, Greg, und Tim Marzullo. How Your Brain Works. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/12429.001.0001.

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Discover the hidden electrical world inside your nervous system using DIY, hands-on experiments, for all ages. No MD or PhD required! The workings of the brain are mysterious: What are neural signals? What do they mean? How do our senses really sense? How does our brain control our movements? What happens when we meditate? Techniques to record signals from living brains were once thought to be the realm of advanced university labs... but not anymore! This book allows anyone to participate in the discovery of neuroscience through hands-on experiments that record the hidden electrical world beneath our skin and skulls. In How Your Brain Works, neuroscientists Greg Gage and Tim Marzullo offer a practical guide—accessible and useful to readers from middle schoolers to college undergraduates to curious adults—for learning about the brain through hands-on experiments. Armed with some DIY electrodes, readers will get to see what brain activity really looks like through simple neuroscience experiments. Written by two neuroscience researchers who invented open-source techniques to record signals from neurons, muscles, hearts, eyes, and brains, How Your Brain Works includes more than forty-five experiments to gain a deeper understanding of your brain. Using a homemade scientific instrument called a SpikerBox, readers can see how fast neural signals travel by recording electrical signals from an earthworm. Or, turning themselves into subjects, readers can strap on some electrode stickers to detect the nervous system in their own bodies. Each chapter begins by describing some phenomenology of a particular area of neuroscience, then guides readers step-by-step through an experiment, and concludes with a series of open-ended questions to inspire further investigation. Some experiments use invertebrates (such as insects), and the book provides a thoughtful framework for the ethical use of these animals in education. How Your Brain Works offers fascinating reading for students at any level, curious readers, and scientists interested in using electrophysiology in their research or teaching. Example Experiments How fast do signals travel down a neuron? The brain uses electricity. . . but do neurons communicate as fast as lightning inside our bodies? In this experiment you will make a speed trap for spikes! Can we really enhance our memories during sleep? Strap on a brainwave-reading sweatband and test the power of cueing up and strengthening memories while you dream away! Wait, that's my number! Ever feel that moment of excitement when you see your number displayed while waiting for an opening at the counter? In this experiment, you will peer into your brainwaves to see what happens when the unexpected occurs and how the brain gets your attention. Using hip hop to talk to the brain. Tired of simply “reading” the electricity from the brain? Would you like to “write” to the nervous system as well? In this experiment you will use a smartphone and hack a headphone cable to see how brain stimulators (used in treating Parkinson's disease) really work. How long does it take the brain to decide? Using simple classroom rulers and a clever technique, readers can determine how long it takes the brain to make decisions.
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