Learning Trends on the Fly in Time Series Data Using Plastic CGP Evolved Recurrent Neural Networks

2018 ◽  
pp. 199-207
Author(s):  
Gul Mummad Khan ◽  
Durr-e-Nayab
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data

A review of irregular time series data handling with gated recurrent neural networks

2021 ◽  
Vol 441 ◽  
pp. 161-178
Author(s):  
Philip B. Weerakody ◽  
Kok Wai Wong ◽  
Guanjin Wang ◽  
Wendell Ela
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Data Handling

scholarly journals Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks

2021 ◽  
Author(s):  
Sibo Cheng ◽  
Mingming Qiu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Dynamical Systems ◽  
Data Assimilation ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Observation Data ◽  
Observation Error ◽  
Error Covariance

AbstractData assimilation techniques are widely used to predict complex dynamical systems with uncertainties, based on time-series observation data. Error covariance matrices modeling is an important element in data assimilation algorithms which can considerably impact the forecasting accuracy. The estimation of these covariances, which usually relies on empirical assumptions and physical constraints, is often imprecise and computationally expensive, especially for systems of large dimensions. In this work, we propose a data-driven approach based on long short term memory (LSTM) recurrent neural networks (RNN) to improve both the accuracy and the efficiency of observation covariance specification in data assimilation for dynamical systems. Learning the covariance matrix from observed/simulated time-series data, the proposed approach does not require any knowledge or assumption about prior error distribution, unlike classical posterior tuning methods. We have compared the novel approach with two state-of-the-art covariance tuning algorithms, namely DI01 and D05, first in a Lorenz dynamical system and then in a 2D shallow water twin experiments framework with different covariance parameterization using ensemble assimilation. This novel method shows significant advantages in observation covariance specification, assimilation accuracy, and computational efficiency.

scholarly journals Learning Time-Series Data of Industrial Design Optimization using Recurrent Neural Networks

2019 ◽  
Author(s):  
Sneha Saha ◽  
Thiago Rios ◽  
Stefan Menzel ◽  
Bernhard Sendhoff ◽  
Thomas Back ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Design Optimization ◽  
Recurrent Neural Networks ◽  
Industrial Design ◽  
Time Series Data ◽  
Series Data ◽  
Learning Time

Bayesian Recurrent Neural Networks for Monitoring Rotorcraft Icing from Aeroacoustics Time-Series Data

2022 ◽  
Author(s):  
Hua Tong ◽  
Jeremiah M. Hauth ◽  
Xun Huan ◽  
Beckett Yx Zhou ◽  
Nicolas R. Gauger ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data

Prediction of Sorghum Biomass Using Uav Time Series Data and Recurrent Neural Networks

2019 ◽  
Author(s):  
Ali Masjedi ◽  
Neal R. Carpenter ◽  
Melba M. Crawford ◽  
Mitch R. Tuinstra
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data

scholarly journals Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes

ScienceRise ◽  
2021 ◽  
pp. 12-20
Author(s):  
Andrii Belas ◽  
Petro Bidyuk
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Autoregressive Models ◽  
Series Data ◽  
Nonstationary Processes ◽  
Modeling And Forecasting ◽  
Technological Product

The object of research. The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Investigated problem. There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear nonstationary processes, such as autoregressive models and recurrent neural networks. However, each of them has its advantages and drawbacks. Autoregressive models cannot deal with the nonlinear or combined influence of previous states or external factors. Recurrent neural networks are computationally expensive and cannot work with sequences of high length or frequency. The main scientific result. The model for forecasting nonlinear nonstationary processes presented in the form of the time series data was built using convolutional neural networks. The current study shows results in which convolutional networks are superior to recurrent ones in terms of both accuracy and complexity. It was possible to build a more accurate model with a much fewer number of parameters. It indicates that one-dimensional convolutional neural networks can be a quite reasonable choice for solving time series forecasting problems. The area of practical use of the research results. Forecasting dynamics of processes in economy, finances, ecology, healthcare, technical systems and other areas exhibiting the types of nonlinear nonstationary processes. Innovative technological product. Methodology of using convolutional neural networks for modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Scope of the innovative technological product. Nonlinear nonstationary processes presented in the form of time-series data.

Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters

2019 ◽  
pp. 488-503 ◽  
Author(s):  
AbdElRahman ElSaid ◽  
Steven Benson ◽  
Shuchita Patwardhan ◽  
David Stadem ◽  
Travis Desell
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Data Prediction

Detecting anomalies in time series data from a manufacturing system using recurrent neural networks

2020 ◽  
Author(s):  
Yue Wang ◽  
Michael Perry ◽  
Dane Whitlock ◽  
John W. Sutherland
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Manufacturing System ◽  
Time Series Data ◽  
Series Data

Recurrent Neural Networks for Time Series Data

2021 ◽  
pp. 175-193
Author(s):  
Ryan G. McClarren
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data
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