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Artificial intelligent models (AIMs) have been successfully adopted in hydrological forecasting in a plenty of literatures. However, the comprehensive comparison of their applicability in particular short-term (i.e. hourly) water level prediction under heavy rainfall events was rarely discussed. Therefore, in this study, the artificial neural networks (ANN), support vector machine (SVM) and adaptive...
A neural network family is commonly used for improving financial forecasting accuracy. This paper proposes a feedback functional link artificial neural network (FFLANN) for the prediction of net asset value (NAV) of Indian Mutual funds which incorporates fewer computational load and fast forecasting capability. It is clear from the root mean square error (RMSE) and mean absolute percentage error (MAPE)...
The worldwide increase in the integration of photovoltaic generation has necessitated improvements in the forecasting approaches. Two models are proposed to cater for PV generation forecasts for few minutes to several hours look-ahead times. A very fast and accurate prediction model based on extreme learning machine is deployed for day-ahead prediction. Moreover, an adaptive and sequential model is...
Improving the use of energy resources has been a great challenge in the last years. A new complex scenario involving a decentralized bidirectional communication between energy suppliers, distribution system and consumption is nowadays becoming reality. Sometimes cited as the largest and most complex machine ever built, Electric Grids (EG) are been transformed into Smart Grids (SG). Hence, the load...
Time series forecasting plays an important role in many fields such as economics, finance, business intelligence, natural sciences, and the social sciences. This forecasting task can be achieved by using different techniques such as statistical methods or Artificial Neural Networks (ANN). In this paper, we present two different approaches to time series forecasting: evolving Takagi-Sugeno (eTS) fuzzy...
This work describes a new approach of the adaptive retraining model for data forecasting. This time, six predictors are simultaneously employed in order to produce a better forecasting for electric load. By doing so, the new forecasting system eliminates iterative simulation. The set of predictors is regularly trained in order to be adjusted to the latest modifications of the input data. The new approach...
This paper attempts to describe hybrid sales forecasting system based on fuzzy clustering and Back-propagation (BP) Neural Networks with adaptive learning rate (FCBPN). The proposed approach is composed of three stages: (1) Winter's Exponential Smoothing method will be utilized to take the trend effect into consideration;(2) utilizing Fuzzy C-Means clustering method, the clusters membership levels...
The paper presents a further improvement of the adaptive retraining procedure of Artificial Neural Networks (ANNs) used for time series predictions. An important advantage of this approach is that the model is periodically adapted to the changes of the non-stationary environment. The retraining starts from proportionally reduced values of the parameters used in the previous version of the ANN model...
For financial investment, the problem that we often encounter is how to extract information hidden in the volatile and noise data and forecast it into future. This study proposes a novel three-stage neural-network-based nonlinear weighted ensemble model. In proposed model, three different types neural-network base models, i.e., Elman network, generalized regression neural network (GRNN) and wavelet...
This study analyzes two implications of the Adaptive Market Hypothesis: variable efficiency and cyclical profitability. These implications are, inter alia, in conflict with the Efficient Market Hypothesis. Variable efficiency has been a popular topic amongst econometric researchers, where a variety of studies have shown that variable efficiency does exist in financial markets based on the metrics...
Limited historical data and large fluctuations are two important issues for forecasting time series. In this paper, a hybrid forecasting model based on adaptive fuzzy time series and particle swarm optimization is proposed to address these issues. In the training phase, the heuristic rules automatically adapt the forecasted values based on trend values and the particle swarm optimization is applied...
The paper proposes a new model for efficient prediction of small and long range exchange rate forecasting. The model employs an adaptive linear combiner with its weights trained using Differential Evolution (DE). A new training scheme of model parameters is proposed using DE based optimization rules. The prediction results are obtained using LMS, GA as well as DE based method. In all cases simulated...
This paper presents an online learning-based neuro-fuzzy system called evolving Fuzzy Ensemble (eFE). The hierarchical computational structure of eFE is progressively adapted to autonomously support fuzzy data associations in accordance with neurophysiological studies. Activity-dependent synapse with global decay learning rule is incorporated to simulate the retention and active forgetting mechanisms...
In this paper, we have presented an adaptive ensemble method for rainfall forecast. The ensemble is adaptive in sense that the members of the ensemble are trained repeatedly. For this purpose, we have employed strategies in repeated one-step ahead prediction rainfall data. On the other hand, we use diverse models and adapt the weights with which each of these models contribute to the ensemble. We...
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