Last edited by Malazshura

Sunday, July 19, 2020 | History

2 edition of **Prediction and geometry of chaotic time series** found in the catalog.

- 397 Want to read
- 26 Currently reading

Published
**1997**
by Naval Postgraduate School, Available from National Technical Information Service in Monterey, Calif, Springfield, Va
.

Written in English

- TIME SERIES ANALYSIS

This thesis examines the topic of chaotic time series. An overview of chaos, dynamical systems, and traditional approaches to time series analysis is provided, followed by an examination of state space reconstruction. State space reconstruction is a nonlinear, deterministic approach whose goal is to use the immediate past behavior of the time series to reconstruct the current state of the system. The choice of delay parameter and embedding dimension are crucial to this reconstruction. Once the state space has been properly reconstructed, one can address the issue of whether apparently random data has come from a low- dimensional, chaotic (deterministic) source or from a random process. Specific techniques for making this determination include attractor reconstruction, estimation of fractal dimension and Lyapunov exponents, and short-term prediction. If the time series data appears to be from a low-dimensional chaotic source, then one can predict the continuation of the data in the short term. This is the inverse problem of dynamical systems. In this thesis, the technique of local fitting is used to accomplish the prediction. Finally, the issue of noisy data is treated, with the purpose of highlighting where further research may be beneficial.

Classifications | |
---|---|

LC Classifications | L5412 |

The Physical Object | |

Pagination | x, 106 p. ; |

Number of Pages | 106 |

ID Numbers | |

Open Library | OL25310342M |

OCLC/WorldCa | 640492871 |

This is a side project of mine, learning more about chaotic time series analysis. My motivation is that I have some time series from my experiment which I would like to analyze. I think some of them are chaotic, whereas others are clearly periodic. I'll add to this web page as I have time . Selecting a time series forecasting model is just the beginning. Using the chosen model in practice can pose challenges, including data transformations and storing the model parameters on disk. In this tutorial, you will discover how to finalize a time series forecasting model and use it to make predictions in Python. After completing this tutorial, you will know: How to finalize a model.

A classic statement of the problem of predicting stationary time series x(t) is as follows [, ].Suppose that a stationary random time series x(t) is defined on time axis t ∈ [∞,+∞]. To simplify the discussion, let us assume that the mean value of the process is zero:Cited by: 3. Geometry From a Time Series. Article. Sep ; performance by the multilayer feedforward neural network in time series prediction. For the similar data selective learning method, we propose a.

Chaotic dynamical systems are ubiquitous in nature and most of them does not have an explicit dynamical equation and can be only understood through the available time series. We here briefly review the basic concepts of time series and its analytic tools, such as dimension, Lyapunov exponent, Hilbert transform, and attractor by: The Mackey-Glass Time Series: The Mackey-Glass time series is a non-linear time delay differential system as shown in (1) Chaotic Time Series Prediction Using Combination of Hidden Markov Model and Neural Nets Saurabh Bhardwaj1, Smriti Srivastava2, J.R.P Gupta3 and Ashish Srivastava4 1,2,3 Netaji Subhas Institute Of Technology, Delhi University,File Size: KB.

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Theory and method of chaotic time series prediction Paperback – January 1, by HAN MIN (Author) See all formats and editions Hide other formats and editions. Price New from Used from Paperback, January 1, "Please retry" Author: HAN MIN.

The book is a summary of a time series forecasting competition that was held a few years ago. The competition used four different kinds of time series (for example, one data set was chaotic from measurements of a laser, and another was a multidimensional physiological times series of heart beats and respiration, etc.).5/5(2).

Enter the password to open this PDF file: Cancel OK. File name:. If the time series data appears to be from a low-dimensional chaotic source, then one can predict the continuation of the data in the short term.

This is the inverse problem of dynamical systems. In this thesis, the technique of local fitting is used to accomplish the : Mary L Leonardi. Prediction of chaotic time series based on the recurrent predictor neural network Abstract: Chaos limits predictability so that the long-term prediction of chaotic time series is very difficult.

The main purpose of this paper is to study a new methodology to model and predict chaotic time series based on a new recurrent predictor neural network Cited by: of the chaotic systems.

The observedscalar time series sig-nals are transformed into a higher dimensional phase space through delay reconstruction (see Sec.

This results in a strange attractor whichis characteristic of the underlying chaotic system. Note that a chaotic signal can be irregu-lar and less predictable in the observed time. prediction of chaotic time series using a combination of Hidden Markov Model (HMM) and Neural Network (NN).

In this paper, an algorithm is proposed wherein an HMM, which is a doubly embedded stochastic process, is used for the shape based clustering. CHAPTER MODELING AND PREDICTING CHAOTIC TIME SERIES Figure Simple averaging method looks up several nearest neighbors of x nin its neighborhood and uses the average of their future images as the prediction ^x n+1 Local Linear Models We can do better than the simple averaging method by building local linear models which show howFile Size: KB.

Figure 34 Geometry of the model thermal pulse combustor. The combustion product is moved into the tailpipe Figure Time series of the four state by: function(RBF) network in chaotic time series predic-tion in early time [2]. Leung and Wang analyzed the structure of hidden-layer in RBFN, and proposed a technique called the cross-validated subspace method to estimate the optimum number of hidden units, and applied the method to prediction of noisy chaotic time series [4].File Size: KB.

Predicting chaotic time series - NASA/ADS. A forecasting technique for chaotic data is presented. After a time series has been embedded in a state space using delay coordinates, the induced nonlinear mapping is 'learned' using a local approximation.

This makes it possible to make short-term predictions of the future behavior of a time series, using information based only on past by: Preprocess Data. In time-series prediction, you use known values of the time series up to point in time, to predict the value at some point in the future.

The standard method for this type of prediction is to create a mapping from sample data points, sampled every units in time (). Lorenz chaotic time series prediction.

The classic Lorenz chaotic time series are introduced to show the process and the results of prediction based on nonlinear correlation function. The Lorenz chaotic time series can be obtained by solving the following three-order ordinary differential equations (ODEs), (19) {x ˙ = α (y − x) y ˙ = (β − z) x − y z ˙ = x y − γ z where, α = 10, β = 28, γ = 8 / : Xiaoxiang Guo, Yutong Sun, Jingli Ren.

Time Series Prediction by Chaotic Modeling of Nonlinear Dynamical Systems - We use concepts from chaos theory in order to model nonlinear dynamical systems that exhibit deterministic behavior.

Observed time series from such a system can be embedded into a higher dimensional phase space without the knowledge of an exact model of the underlying dynamics. The theory of chaos plays an important role in economics, finance, nonlinear circuit and weather forecasting.

Especially, the prediction of chaotic time series is meaningful to science research and our daily life. However, people always predict the value of a segment of time series by using the known time series before from the same by: Chaotic Time Series Prediction by Deshpande Rohit,available at Book Depository with free delivery worldwide.

Neural networks have been widely employed for chaotic time series prediction [4], [5], [6], [7]. Other methods are also known to be useful for time-series forecasting, such as Kalman filtering [8.

Chaotic Time Series Prediction by Fusing Local Methods Yong Wang, Shiqiang Hu* School of Aeronautics and Astronautics Shanghai Jiao Tong University, Shanghai [email protected], [email protected] Abstract—In this paper, a novel prediction algorithm is proposed to predict chaotic time series.

The chaotic time series can beFile Size: KB. RAPID COMMUNICATIONS PHYSICAL REVIEW E 92, (R) () Predicting chaotic time series with a partial model Franz Hamilton, 1Tyrus Berry, 2 and Timothy Sauer,* 1Department of Electrical and Computer Engineering and Department of Mathematical Sciences, George Mason University, Fairfax, VirginiaUSA 2Department of Mathematics, Pennsylvania State University, File Size: KB.

Chaotic Time Series Prediction Based on a Novel Robust Echo State Network Abstract: In this paper, a robust recurrent neural network is presented in a Bayesian framework based on echo state mechanisms. Since the new model is capable of handling outliers in the training data set, it is termed as a robust echo state network (RESN).Cited by:.

Chaos and Time-Series Analysis J. C. Sprott A web page supplement to the book by the above title. This page contains supplementary materials, computer software, color figures, animations, errata, and links to web resources for the text Chaos and Time-Series Analysis (Oxford University Press, ).It is organized according to the chapters in the book.1.

Introduction. Chaotic time series is a kind of nonlinear dynamic phenomenon between certainty and randomness, in which Lyapunov exponent is adopted to decide whether a time series is chaos or not; that is, the time series is chaotic if its Lyapunov exponent is greater than zero [].Because it can be widely applied in real life, such as in the network traffic, earthquake prediction, and Cited by: 3.Forecasting of Chaotic Time Series Using RBF Neural Networks Optimized By Genetic Algorithms.

Mohammed Awad. Faculty of Engineering and Information Technology, Arab American University, Palestine. Abstract: Time series forecasting is an important tool, which used to support the areas of planning is for both individual and organizational File Size: KB.