Comparative analysis of autoregressive approaches and recurrent neural networks for modeling and forecasting nonlinear nonstationary processes

Belas, Oleg; Бідюк, Петро Іванович; Belas, Andrii

doi:10.20535/2411-1031.2019.7.1.184395

Cited by 3 publications

(8 citation statements)

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“…The task of forecasting processes in technical systems is very deeply analyzed in literature using classical regression approaches, which are quite simply used both from a theoretical and a computational point of view [2,3,20,21]. However, this approach has drawbacks, because it cannot take into account a large number of external factors, due to the problem of multicollinearity, moreover, if they show nonlinear interaction [4].…”

Section: Discussionmentioning

confidence: 99%

“…However, the application of this approach requires large computational costs, and this approach cannot be applied to very long sequences, what creates the problem for modern studies with the use of big data [4].…”

Section: Discussionmentioning

confidence: 99%

“…ematical models based on linear self-dependence within time series (autocorrelation), which is able to explain future fluctuations [2,3]. However, this approach is limited by the difficulty of taking into account a large number of external factors, due to the problem of multicollinearity; in addition, it also can't model nonlinear interactions [4].…”

Section: Introductionmentioning

confidence: 99%

“…But, as recent research has shown, there is little use of these benefits in practice. Applying this approach requires a lot of computational costs, so this approach cannot be applied to very long sequences, which is a problem for solving modern problems using large amounts of data [4].…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes

Belas

Bidyuk

2021

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes

Belas

Bidyuk

2021

Self Cite

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“…У праці [1] встановлено, що завдання прогнозування процесів у технічних системах ґрунтовно проаналізовано із застосуванням класичних регресійних підходів, які доволі просто використовуються як у теоретичному аспекті, так і в обчислювальному. Проте такий підхід має певні недоліки.…”

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

Belas¹,

Belas²

2021

SRIT

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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.

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Choosing a Quality Criterion for Evaluating the Forecast of Nonlinear Non-Stationary Processes

Belas¹,

Bidyuk²

2021

KPI SN

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Background. The problem of forecasting nonlinear nonstationary processes presented in the form of time series is very relevant, since such series can describe dynamics of the processes in both technical and economic systems. To establish the best model, various metrics are used to assess the quality of forecasts, such as R^2, RMSE, MAE, MAPE. However, in many tasks, when optimizing the model according to the selected criterion, the model becomes worse in relation to another criterion. Therefore it is important to understand which metric must be used to optimize and assess the quality of the forecast in the given task. Objective. The aim of the paper is to develop a criteria base for assessing forecasts of nonlinear nonstationary processes, as well as an approach to choosing a metric in accordance to the specificity of the set forecasting problem. Methods. The paper presents a comparative analysis of the basic metrics for the regression problem, their theoretical and practical meaning, advantages and disadvantages in various cases. New approaches are proposed based on the results of the analysis. Results. Based on the analysis of the selected data, it is shown that by optimizing the model according to the selected criterion, the model becomes worse in relation to another criterion. A criterion basis for assessing forecasts of nonlinear nonstationary processes has been formed, as well as an approach to the selection of a quality criterion in accordance with the specifics of the set forecasting problem. To minimize an absolute error, the RMSE (MSE, R^2) and MAE metrics are analysed and recommended, depending on the need to work with outliers. The RMSLE metric is proposed for solving the problems of minimizing the relative metric, for solving the shown problems of the MAPE metric for this class of problems. Conclusions. The paper shows the importance of choosing a metric that must be used to optimize and assess the quality of the forecasts in the given task. The obtained criterion base and approach can be used in further research to solve practical prob- lems in modelling and forecasting nonlinear nonstationary processes and to develop new methods or general method for solving such problems.

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Comparative analysis of autoregressive approaches and recurrent neural networks for modeling and forecasting nonlinear nonstationary processes

Cited by 3 publications

References 5 publications

Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes

Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes

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

Choosing a Quality Criterion for Evaluating the Forecast of Nonlinear Non-Stationary Processes

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