Forecasting of nonlinear time series using ANN

Tealab, Ahmed; Hefny, Hesham A.; Badr, Amr

doi:10.1016/j.fcij.2017.05.001

Cited by 125 publications

(70 citation statements)

References 15 publications

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“…There are many ways to normalize feature values in order to scale them to a single range and use them in various machine learning models. Depending on the function used, they can be divided into two large groups: linear and non-linear [Tealab et al, 2017]. With nonlinear normalization, the calculated ratios use the functions of the logistic sigmoid or hyperbolic tangent.…”

Section: Data Normalizationmentioning

confidence: 99%

Methodology of physiography zoning using machine learning: A case study of the Black Sea

Krivoguz¹

2020

Russ. J. Earth Sci.

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Problem of area's zoning is very important and is one of the main problems of modern geographical science. Our point is to from a modern approach, based on the machine learning methods to provide zoning of any area. Key ideas of this methodology, that any distribution of factors that form any geographical system grouped around some clusters-unique zones that represents specific nature conditions. Formed methodology based on several stages-selection of data and objects for analysis, data normalization, assessment of predisposition of data for clustering, choosing the optimal number of clusters, clustering and validation of results. As an example, we tried to zone a surface layer of the Black Sea. We find that optimal number of unique zones is 3. Also, we find that the key driver of zone forming is a location of the rivers. Thus, we can say, that applying a machine learning approach in area's zoning tasks helps us increasing the quality of nature using and decision-making processes.

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Section: Data Normalizationmentioning

confidence: 99%

Methodology of physiography zoning using machine learning: A case study of the Black Sea

Krivoguz¹

2020

Russ. J. Earth Sci.

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“…Various authors propose an improvement in forecasts based on time series analysis (in particular for nonstationary series) using the artificial intelligence [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of hydropower ratio from total energy production in Romania

et al. 2019

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It is important to know the structure of electricity production of a country and to establish the place of each source within the total energy mix. The aim of this paper is to analyze statistically the percentage of the hydropower production related to the total electrical energy produced in Romania. The time series data analysis was done to determine the trends of average and standard deviation of hydro energy data produced in Romania over eleven years' period (2006-2016).

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“…Artificial neural networks are recognized by scientists as efficient forecasting tools as there are a lot of scientific papers that demonstrate their superiority for the forecasting accuracy when compared to other methods like statistical ones [10][11][12][13]. There are many papers that cover a wide range of artificial neural network implementations that prove their undeniable advantages in terms of performing specific tasks efficiently, with very good results [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The forecasting of time series is extensively covered in numerous scientific works [15][16][17][18][20][21][22][23][24]. In the case of non-linear systems and systems whose state varies dynamically, being influenced by their actual state, methods like the nonlinear autoregressive and the nonlinear autoregressive with exogenous inputs proved to be very efficient in forecasting future values of time series [19,25].…”

Section: Introductionmentioning

confidence: 99%

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

et al. 2017

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This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

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Forecasting of nonlinear time series using ANN

Cited by 125 publications

References 15 publications

Methodology of physiography zoning using machine learning: A case study of the Black Sea

Methodology of physiography zoning using machine learning: A case study of the Black Sea

Analysis of hydropower ratio from total energy production in Romania

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

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