scholarly journals General methods of forecasting nonlinear nonstationary processes based on mathematical models using statistical data

2021 ◽  
pp. 79-86
Author(s):  
Oleg Belas ◽  
Andrii Belas
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Mathematical Models ◽  
Recurrent Neural Networks ◽  
Statistical Data ◽  
Economic Systems ◽  
Nonstationary Processes ◽  
General Technique ◽  
New Approach ◽  
Advantages And Disadvantages

The article considers the problem of forecasting nonlinear nonstationary processes, presented in the form of time series, which can describe the dynamics of processes in both technical and economic systems. The general technique of analysis of such data and construction of corresponding mathematical models based on autoregressive models and recurrent neural networks is described in detail. The technique is applied on practical examples while performing the comparative analysis of models of forecasting of quantity of channels of service of cellular subscribers for a given station and revealing advantages and disadvantages of each method. The need to improve the existing methodology and develop a new approach is formulated.

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.

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 Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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.

Using Genetic Programming to Extract Knowledge from Artificial Neural Networks

2008 ◽  
pp. 308-327
Author(s):  
Daniel Rivero ◽  
Miguel Varela ◽  
Javier Pereira
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Recurrent Neural Networks ◽  
Know How ◽  
Artificial Neural

A technique is described in this chapter that makes it possible to extract the knowledge held by previously trained artificial neural networks. This makes it possible for them to be used in a number of areas (such as medicine) where it is necessary to know how they work, as well as having a network that functions. This chapter explains how to carry out this process to extract knowledge, defined as rules. Special emphasis is placed on extracting knowledge from recurrent neural networks, in particular when applied in predicting time series.

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