A two-step combined algorithm based on NARX neural network and the subsequent prediction of the residues improves prediction accuracy of the greenhouse gases concentrations

Buevich, Alexander; Sergeev, Alexander; Shichkin, Andrey; Baglaeva, Elena

doi:10.1007/s00521-020-04995-4

Cited by 30 publications

(10 citation statements)

References 33 publications

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“…As more studies for more approaches, Li [30] improved the accuracy of ANN models through combing the above networks with intelligent diagnosis in medical treatments where the combined model improved diagnosis efficiency and saved doctors' time. Instead of outputs, the residuals from a two-step combined model are estimated, and the accuracy of the prediction is determined by the set of errors and indices [31]. The prediction accuracy of neural networks can also be improved by composing them with time-series data as well as developing hybrid methods [32].…”

Section: Performances Of Neural Network Modelsmentioning

confidence: 99%

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Mahmoudabadi¹,

Kanaani

Ghazimahalleh³

2022

HighTech. Innov. J.

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Investment experts, who deal with stock price estimation, commonly look for the most accurate and appropriate statistical techniques to make decisions on investment. The aim of this study is to improve the accuracy of stock price prediction models through modifying the structure of a combined neural network model with time-series data, in which the main contribution is to insert the time-series analysis prediction into the hidden layer of the neural network. The proposed structure is made up of neural networks and time-series analysis, with variable reduction used to remove attributes with inter-correlations. Data has been collected over six years (72 months) from the Iranian stock market, including the number of trades, new-coin price, gold-18 price, US Dollar and Euro equivalent currencies, oil-index price, Brent-oil price, industry index, and balanced stock index, followed by developing the prediction models. Comparing the performance criteria of the proposed structure to the traditional ones in terms of the mean square and mean absolute errors revealed that inserting time-series estimated variables into hidden layers would improve the performance of neural network models to estimate stock prices for making investment decisions. Doi: 10.28991/HIJ-2022-03-01-05 Full Text: PDF

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Section: Performances Of Neural Network Modelsmentioning

confidence: 99%

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Mahmoudabadi¹,

Kanaani

Ghazimahalleh³

2022

HighTech. Innov. J.

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“…Однако необходимо отметить, что большинство как российских, так и зарубежных исследований связаны именно с технологиями искусственного интеллекта, их разработкой, внедрением и улучшением. Есть работы по отдельным направлениям ИИ, например, можно отметить ряд трудов российских исследователей в сфере ИИ -компьютерное зрение (Khokhlova et al, 2019;Bokovoy et al, 2020), биометрическое распознавание (Vartanov et al, 2020), предиктивная аналитика (Buevich et al, 2021) и др.…”

Section: анализ существующих подходов к классификации технологий в сф...unclassified

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2022

ESC / ЭиСП

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Рецензируемый научно-практический журнал, охватывающий вопросы анализа и прогноза изменений в экономике и социальной сфере различных стран и регионов, локальных территорий.Основная цель издания журнала -предоставление широким слоям мировой научной общественности и практическим работникам возможности публиковать результаты изысканий в сфере исследования социально-экономических процессов, знакомиться с различными точками зрения на актуальные проблемы развития экономики и социума, принимать участие в дискуссиях по обсуждаемым темам. В числе основных тем -стратегии развития территорий, региональная и отраслевая экономика, социальное развитие, вопросы формирования доходов бюджетов и рационализации расходов, инновационная экономика, вопросы экономической теории.

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“…The NARX neural network technique is an important class of nonlinear recurrent dynamic ANN computer program networks comprising linked nodes inspired by a simplification of the human neural system. As a result, every point contains an artificial neuron that takes one or more inputs and accumulates them and passes through a nonlinear active function to generate an output [32][33][34][35]. In the Feedforward Neural Networks (FNNs) model, the information moves in one direction, with nodes structured in layers.…”

Section: Narx Neural Network Modelmentioning

confidence: 99%

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

2021

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In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process control. Moreover, the NARX method is selected because of its quick learning and completion times, as well as high appropriateness, and is distinguished by advantageous dynamics and interference resistance. The neural network (NN) is trained and optimized with three algorithms, the Levenberg–Marquardt Algorithm (NARX-LMA), the Bayesian Regularization Algorithm (NARX-BRA) and the Scaled Conjugate Gradient Algorithm (NARX-SCGA), to attain the best performance. The forecasted results using the NARX method based on the three algorithms are compared with experimentally measured data. The NARX-LMA, NARX-BRA and NARX-SCGA models are validated using statistical criteria. In general, weather conditions have a significant impact on the execution and quality of the results.

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A two-step combined algorithm based on NARX neural network and the subsequent prediction of the residues improves prediction accuracy of the greenhouse gases concentrations

Cited by 30 publications

References 33 publications

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

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Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

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