Analysis of the predictive ability of time delay neural networks applied to the S&P 500 time series

2000 ◽  
Vol 30 (4) ◽  
pp. 568-572 ◽  
Author(s):  
R. Sitte ◽  
J. Sitte
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Delay ◽  
Predictive Ability

DYNAMICAL RECURRENT NEURAL NETWORKS — TOWARDS ENVIRONMENTAL TIME SERIES PREDICTION

1995 ◽  
Vol 06 (02) ◽  
pp. 145-170 ◽  
Author(s):  
ALEX AUSSEM ◽  
FIONN MURTAGH ◽  
MARC SARAZIN
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Prediction ◽  
Predictive Ability ◽  
Complex Problem ◽  
Internal Variables ◽  
Internal Dynamic ◽  
K Nearest Neighbors ◽  
Fuzzy Coding

Dynamical Recurrent Neural Networks (DRNN) (Aussem 1995a) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal-recurrent-backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meteorological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode. In this perspective, the model is first appraised on precipitation measurements with traditional nonlinear AR and ARMA techniques using feedforward networks. Then we tackle a complex problem, namely the prediction of astronomical seeing, known to be a very erratic time series. A fuzzy coding approach is used to reduce the complexity of the underlying laws governing the seeing. Then, a fuzzy correspondence analysis is carried out to explore the internal relationships in the data. Based on a carefully selected set of meteorological variables at the same time-point, a nonlinear multiple regression, termed nowcasting (Murtagh et al. 1993, 1995), is carried out on the fuzzily coded seeing records. The DRNN is shown to outperform the fuzzy k-nearest neighbors method.

scholarly journals On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 584
Author(s):  
Riccardo Rossi ◽  
Andrea Murari ◽  
Pasquale Gaudio
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Delay ◽  
Causal Relationship ◽  
Gaussian Noise ◽  
The Other ◽  
Causal Relationships ◽  
Indirect Coupling ◽  
Active Research

Determining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling between three Lorenz systems is investigated with the help of specifically designed artificial neural networks, called time delay neural networks (TDNNs). TDNNs can learn from their previous inputs and are therefore well suited to extract the causal relationship between time series. The performances of the TDNNs tested have always been very positive, showing an excellent capability to identify the correct causal relationships in absence of significant noise. The first tests on the time localization of the mutual influences and the effects of Gaussian noise have also provided very encouraging results. Even if further assessments are necessary, the networks of the proposed architecture have the potential to be a good complement to the other techniques available in the market for the investigation of mutual influences between time series.

scholarly journals Time-Series Prediction for Amount of Airworthiness Based on Time-Delay Neural Networks

2020 ◽  
Vol 26 (5) ◽  
pp. 28-32
Author(s):  
Ali Tatli ◽  
Sinem Kahvecioglu ◽  
Hikmet Karakoc
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Delay ◽  
Meteorological Parameters ◽  
Time Series Prediction ◽  
Flight Training ◽  
Weather Events ◽  
Visual Flight Rules ◽  
Different Types ◽  
Different Levels

Troposphere and the first stratum of the stratosphere are intensely utilized atmosphere layers for the aviation activities. Due to the different performances, capabilities, designs, and equipment of the aerial vehicles, meteorological weather events that occur in the troposphere affect these vehicles at different levels during their aeronautical activities. Although simple aircrafts are more sensitive to the effects of meteorological events, they are especially preferred by flight training organizations (FTOs) in pilotage training when they are considered in terms of maintenance and equipment costs. In cases where inexperienced pilot candidates and simple aircrafts that are more vulnerable to weather events come together, analysis and prediction of meteorological parameters becomes more important in terms of preventing accidents and reducing risks, as well as proper planning for flight and maintenance. The purposes of this study are, first, to derive flight availability time-series for two different types of aircraft according to visual flight rules by using Meteorological Terminal Air Report (METAR), and then to establish and evaluate a prediction model by using Time-Delay Neural Networks (TDNNs).

A Methodology for the Modeling of Rail Vehicles From Time Series Measurements Using Time-Delay Neural Networks

2007 ◽  
Author(s):  
John Zolock ◽  
Robert Greif
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Delay ◽  
Phase Space ◽  
Dynamical Systems ◽  
Dynamic Systems ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Modeling Methodology ◽  
Forcing Function

The main goal of this research is to develop and demonstrate a general, efficient, mathematically and theoretically based methodology to model nonlinear forced vibrating mechanical systems from time series measurements. A system identification modeling methodology for forced dynamical systems is presented based on dynamic system theory and nonlinear time series analysis that employs phase space reconstruction (delay vector embedding) for modeling of dynamical systems from time series data using time-delay neural networks (TDNN). The first part of this work details the modeling methodology including background on dynamic systems, phase space reconstruction, and neural networks. In the second part of this work the methodology is evaluated based on its ability to model selected analytical lumped parameter forced vibrating dynamic systems including an example of a linear system predicting lumped mass displacement using a displacement forcing. function The work discusses the application to nonlinear systems, multi degree-of-freedom systems, and multi-input systems. The methodology is further evaluated on its ability to model an analytical passenger rail vehicle predicting vertical wheel/rail force using vertical rail profile as input. Studying the neural modeling methodology using an analytical systems shows the clearest observations from results which provide prospective users of this tool an understanding of the expectations and limitations of the modeling methodology.

A Methodology for the Modeling of Forced Dynamical Systems From Time Series Measurements Using Time-Delay Neural Networks

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
John Zolock ◽  
Robert Greif
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Delay ◽  
Phase Space ◽  
Dynamical Systems ◽  
Dynamic Systems ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Modeling Methodology ◽  
Forcing Function

The main goal of this research was to develop and present a general, efficient, mathematical, and theoretical based methodology to model nonlinear forced-vibrating mechanical systems from time series measurements. A system identification modeling methodology for forced dynamical systems is presented based on a dynamic system theory and a nonlinear time series analysis that employ phase space reconstruction (delay vector embedding) in modeling dynamical systems from time series data using time-delay neural networks. The first part of this work details the modeling methodology, including background on dynamic systems, phase space reconstruction, and neural networks. In the second part of this work, the methodology is evaluated based on its ability to model selected analytical lumped-parameter forced-vibrating dynamic systems, including an example of a linear system predicting lumped mass displacement subjected to a displacement forcing function. The work discusses the application to nonlinear systems, multiple degree of freedom systems, and multiple input systems. The methodology is further evaluated on its ability to model an analytical passenger rail car predicting vertical wheel∕rail force using a measured vertical rail profile as the input function. Studying the neural modeling methodology using analytical systems shows the clearest observations from results, providing prospective users of this tool an understanding of the expectations and limitations of the modeling methodology.

Sign in / Sign up

Export Citation Format

Share Document