Nonlinear Forecasting Using Factor-Augmented Models

2011 ◽  
Vol 32 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Bruno Cara Giovannetti
Keyword(s):  
Nonlinear Forecasting

Nonlinear forecasting of non-uniform chaotic attractors in an enzyme reaction

1994 ◽  
Vol 348 (1688) ◽  
pp. 421-430
Keyword(s):  
Time Series ◽  
Enzyme Reaction ◽  
Experimental Conditions ◽  
Biological Time ◽  
Alternative Means ◽  
Nonlinear Forecasting ◽  
Linear Predictors ◽  
Short Term Forecasting ◽  
Low Dimensional ◽  
Frequent Sampling

Nonlinear forecasting was used to predict the time evolution of fluctuating concentrations of dissolved oxygen in the peroxidase-oxidase reaction. This reaction entails the oxidation of NADH with molecular oxygen as the electron acceptor. Depending upon the experimental conditions, either regular or highly irregular oscillations obtain. Previous work suggests that the latter fluctuations are almost certainly chaotic. In either case, the dynamics contain multiple timescales, which fact results in an uneven distribution of points in the phase space. Such ‘nonuniformity,’ as it is called, is a rock on which conventional methods for analysing chaotic time series often founder. The results of the present study are as follows. 1. Short-term forecasting with local linear predictors yields results that are consistent with a hypothesis of low-dimensional chaos. 2. Most of the evidence for nonlinear determinism disappears upon the addition of small amounts of observational error. 3. It is essentially impossible to make predictions over time intervals longer than the average period of oscillation for time series subject to continuous and frequent sampling. 4. Far more effective forecasting is possible for points on Poincare sections. 5. An alternative means for improving forecasting efficacy using the continuous data is to include a second variable (NADH concentration) in the analysis. Since non-uniformity is common in biological time series, we conclude that the application of nonlinear forecasting to univariate time series requires care both in implementation and interpretation.

Period-Adding Bifurcation with Chaos in the Interspike Intervals Generated by an Experimental Neural Pacemaker

1997 ◽  
Vol 07 (08) ◽  
pp. 1867-1872 ◽  
Author(s):  
W. Ren ◽  
S. J. Hu ◽  
B. J. Zhang ◽  
F. Z. Wang ◽  
Y. F. Gong ◽  
...  
Keyword(s):  
Neural Coding ◽  
Calcium Concentration ◽  
Channel Blocker ◽  
Interspike Interval ◽  
Surrogate Data ◽  
Nonlinear Phenomena ◽  
Chaotic Oscillations ◽  
Interspike Intervals ◽  
Excitable Cells ◽  
Nonlinear Forecasting

The dynamics of the generation of the various spike trains in neural pacemakers is of fundamental importance to the understanding of neural coding. Recent studies have demonstrated, theoretically and experimentally, that neural pacemakers produce chaotic oscillations. Deeper analyses in several neuronal models have revealed many nonlinear phenomena including period-adding bifurcations whose existence has not been experimentally confirmed. In this letter, we reported that the period-adding bifurcation with chaos was observed in the interspike interval (ISI) series generated by an experimental neural pacemaker when the extracellular calcium concentration was changed or a potassium channel blocker was administered at the site of the pacemaker. We also simulated our experimental discoveries by computing a generalized model of excitable cells. The chaotic phenomenon in the experiment and that in the model were demonstrated and compared using the nonlinear forecasting and surrogate data methods.

Circumventing structural uncertainty: A Bayesian perspective on nonlinear forecasting for ecology

2017 ◽  
Vol 32 ◽  
pp. 134-143 ◽  
Author(s):  
Stephan B. Munch ◽  
Valerie Poynor ◽  
Juan Lopez Arriaza
Keyword(s):  
Structural Uncertainty ◽  
Nonlinear Forecasting

Nonlinear Models Used to Analyze the Relation between Inflation and Unemployment

2020 ◽  
Vol 11 (2) ◽  
pp. 667
Author(s):  
Laura UNGUREANU ◽  
Madalina CONSTANTINESCU ◽  
Cristina POPÎRLAN
Keyword(s):  
Neural Networks ◽  
Mathematical Models ◽  
Nonlinear Models ◽  
Numerical Data ◽  
Optimization Models ◽  
Numerical Application ◽  
Forecasting Methods ◽  
Nonlinear Forecasting ◽  
The Relationship

Many mathematical models have been developed in the last years in order to analyze economic phenomena and processes. Some of these models are optimization models, static or dynamic, while others are developed specially to study the evolution of economic phenomena. The topic of this paper is forecasting with nonlinear models. A few well-known nonlinear models are introduced, and their properties are discussed. The variety of nonlinear relationships is important both from the perspective of estimation and from the precision of forecasts in the medium and especially long term. Most nonlinear forecasting methods and all methods based on neural networks lead to predictions that have a better quality than the forecasts obtained by linear methods. The last section of this paper contains a detailed study of the relationship between inflation and unemployment and a numerical application with numerical data from Romania.

Measles as a case study in nonlinear forecasting and chaos

1994 ◽  
Vol 348 (1688) ◽  
pp. 515-530 ◽  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Developed Countries ◽  
Epidemiological Models ◽  
Before And After ◽  
High Predictability ◽  
Nonlinear Forecasting ◽  
Measles Incidence

This paper uses measles incidence in developed countries as the basis of a case study in nonlinear forecasting and chaos. It uses a combination of epidemiological modelling and nonlinear forecasting to explore a range of issues relating to the predictability of measles before and after the advent of mass vaccination. A comparison of the pre-vaccination self-predictability of measles in England and Wales indicates relatively high predictability of these predominantly biennial epidemic series, compared to New York City, which shows mixtures of one-, twoand three-year epidemics. This analysis also indicates the importance of choosing correct embeddings to avoid bias in prediction. Forecasting for English cities indicates significant spatial heterogeneity in predictability before vaccination and an overall drop in predictability during the vaccination era. The interpretation of predictions of observed measles series by epidemiological models is explored and areas for refinement of current models discussed.

Technique for Magnetic Bearing Based on Mixed-Kernel Support Vector Machine Forecasting Model

2014 ◽  
Vol 529 ◽  
pp. 349-353 ◽  
Author(s):  
Jie Jin ◽  
Huang Qiu Zhu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Magnetic Bearing ◽  
The Self ◽  
Support Vector ◽  
Forecasting Model ◽  
Kernel Support Vector Machine ◽  
Vector Machines ◽  
Nonlinear Forecasting ◽  
The Cost

The self-sensing magnetic bearing can reduce the cost and the axial size of the magnetic bearing and increase its reliability. A mixed-kernel least squares support vector machines (LS-SVM) forecasting model is proposed for self-sensing technique of a hybrid magnetic bearing. The structure and mathematical model of the radial-axial hybrid magnetic bearing are introduced. Based on the principle of the mixed-kernel LS-SVM, the nonlinear forecasting model between the current and the displacement which realizes the displacement self-sensing control is built through genetic algorithm. Simulation has done to verify the validity and feasibility of proposed method.

scholarly journals Chaos Theory Methods for Contact-Center Dinamic Control

2021 ◽  
Vol 7 (2) ◽  
pp. 18-23
Author(s):  
A. Goldstein ◽  
S. Kislyakov ◽  
M. Fenomenov
Keyword(s):  
Chaos Theory ◽  
Chaotic Behavior ◽  
Optimal Number ◽  
Contact Center ◽  
Global Approximation ◽  
Nonlinear Forecasting ◽  
Lyapunov Index ◽  
Optimal Control Methods ◽  
Financial Losses

The work is devoted to searching for optimal control methods for contact center, in particular, methods for predicting the load for further calculation of required number of operators. If the number of operators is always more than required, then the owners of the contact center will incur financial losses. If there are too few employees, the quality of service will decline. Predicting the load of the contact center is required in order to bring the optimal number of operators to work in advance. It is proposed to apply chaos theory to predict the incoming load of a contact center. Positive value of the Lyapunov index indicates the chaotic behavior of the input flow of the load. To predict the load, the methods of linear and nonlinear forecasting and the method of global approximation are used. The paper presents the results of comparing these methods for the problem of predicting the incoming load of contact center.

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