A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks

Papageorgiou, Elpiniki I.; Poczęta, Katarzyna

doi:10.1016/j.neucom.2016.10.072

Cited by 68 publications

(40 citation statements)

References 40 publications

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“…A range of different learning algorithms can be employed in ANN. Herein the neural networks are constructed within MATLAB using the Levenberg-Marquardt back-propagation algorithm (in which the sum of the squares of the deviations between the observations and model predictions is minimized) (Papageorgiou and Poczeta, 2017). Although there is no single "best-practice" regarding the number of hidden layers to use with ANN, there is evidence that a single hidden layer is sufficient for the large majority of problems (Toth et al, 2000).…”

Section: Methodsmentioning

confidence: 99%

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017

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Abstract. The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called "warming holes" (i.e., locations with decreasing daily maximum air temperatures (T ) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θ e ) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θ e are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (SM). SM is particularly important in determining the magnitude of θ e over regions that have previously been identified as exhibiting warming holes, confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θ e . Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T , synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in minimum and maximum θ e , where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θ e and the occurrence of extreme maximum θ e . Over the entire domain, the ANN with three hidden layers exhibits high accuracy in predicting maximum θ e > 347 K. The median hit rate for maximum θ e > 347 K is > 0.60, while the median false alarm rate is ≈ 0.08.

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Section: Methodsmentioning

confidence: 99%

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017

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“…It is an effective tool for modeling decision support systems. FCMs have been applied in many research domains, e.g., in business performance analysis [72], strategy planning [73], modeling virtual worlds [74], time series prediction [69], and adoption of educational software [75].…”

Section: Fuzzy Cognitive Maps Overviewmentioning

confidence: 99%

“…Time series forecasting is a highly important and dynamic research domain, which has wide applicability to many diverse scientific fields, ranging from ecological modeling to energy [1], on the efficient capabilities of evolutionary fuzzy cognitive maps (FCMs) and enhanced by structure optimization algorithms and artificial neural networks (ANNs), was introduced in [69]. Furthermore, the researchers in [21,60] recently conducted a preliminary study on implementing FCMs with NNs for natural gas prediction.…”

Section: Introductionmentioning

confidence: 99%

Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece

et al. 2019

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This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.

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“…Подход имеет две основные стадии [17]. На первой стадии разрабатывается модель нечеткой когни-тивной карты по историческим данным временных рядов с помощью генетического алгоритма обучения.…”

Section: гибридный подход к прогнозированию на основе нечетких когнитunclassified

Гибридные Нечеткие Когнитивные Карты В Задачах Поддержки Принятия Решений И Прогнозирования

Ярушев¹,

Yarushev²,

Averkin³

et al. 2017

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A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks

Cited by 68 publications

References 40 publications

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece

Гибридные Нечеткие Когнитивные Карты В Задачах Поддержки Принятия Решений И Прогнозирования

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