Search Results - (( variable forecasting model algorithm ) OR ( java application optimization algorithm ))

A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…The firefly algorithm remains a feasible alternative for shallow architectural network models, while metaheuristic algorithms such as the Particle swarm algorithm and Bat algorithm are better options for deeper architectural network models. …”
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Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…The results show that the major findings regarding the model performance during the simulation period indicate the necessity to pay attention to evaluating the optimized model performance by considering the results of the forecasting model for both the hydrological variables of reservoir inflow and reservoir evaporation rather than the deterministic values.…”
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Automated time series forecasting by Ismail, Suzilah, Zakaria, Rohaiza, Tuan Muda, Tuan Zalizam

“…While quantitative technique is based on statistical concepts and requires large amount of data in order to formulate the mathematical models.This technique can be classified into projective and causal technique.The projective technique (or univariate modelling) just involve one variable while the causal technique (or econometric modelling) suitable for multi-variables.Since forecasting involves uncertainty, several methods need to be executed on one set of time series data in order to produce accurate forecast.Hence, usually in practice forecaster need to use several softwares to obtain the forecast values.If this practice can be transformed into algorithm (well-defined rules for solving a problem) and then the algorithm can be transformed into a computer program, less time will be needed to compute the forecast values where in business world time is money.In this study, we focused on algorithm development for univariate forecasting techniques only and will expand towards econometric modelling in the future.Two set of simulated data (yearly and non-yearly) and several univariate forecasting techniques (i.e. …”
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Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search by Wahab, Musa

“…The first problem is the fitness evaluation in the electricity demand forecasting model in which more than one variable are included which leads to increase the sum of squared deviations. …”
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Algorithmic approaches in model selection of the air passengers flows data by Ismail, Suzilah, Yusof, Norhayati, Tuan Muda, Tuan Zalizam

“…Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts.…”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…Furthermore, EEC in ASEAN-5 countries is forecasted by autoregressive integrated moving average (ARIMA) model and first-order single-variable grey model (GM (1, 1)) and their forecasts are compared with those obtained by the proposed method.…”
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Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks by Islam, B., Baharudin, Z., Nallagownden, P.

“…The proposed input variable selection approach not only improves that forecast accuracy but also reduces the computational efforts and training time of forecasting models. …”
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Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm by Pham Q.B., Afan H.A., Mohammadi B., Ahmed A.N., Linh N.T.T., Vo N.D., Moazenzadeh R., Yu P.-S., El-Shafie A.

Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting by Afan H.A., Allawi M.F., El-Shafie A., Yaseen Z.M., Ahmed A.N., Malek M.A., Koting S.B., Salih S.Q., Mohtar W.H.M.W., Lai S.H., Sefelnasr A., Sherif M., El-Shafie A.

Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf by Abdul Manaf, Nor Salam

“…Multiple independent variables are used in a more intricate forecasting model called multiple linear regression to predict a dependent variable.…”
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Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

“…One was the fitness evaluation in the demand forecasting model in which more than one variable was included, and the other was accuracy of the demand forecasting model to predict the future projection of random energy demand. …”
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SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

“…The SURE-Autometrics is also validated using two sets of real data by comparing the forecast error measures with five model selection algorithms and three non-algorithm procedures. …”
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Multi-horizon ternary time series forecasting by Htike@Muhammad Yusof, Zaw Zaw

“…This is mainly because state-of-the-art forecasting algorithms essentially perform single-horizon forecasts and produce continuous numbers as outputs. …”
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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei by Alireza , Pourdaryaei

“…The main challenge in this area is providing highly accurate and efficient day-ahead price forecasting. A suitable feature selection technique, which is able to model the interacting features and nonlinearities of the forecast processes, is still required although researches have been performed for day-ahead forecasting. …”
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Weather prediction in Kota Kinabalu using linear regressions with multiple variables by Teong, Khan Vun, Chung, Gwo Chin, Jedol Dayou

“…This study employs machine learning algorithms, a linear regression model using statistics, and two optimization approaches, the normal equation approach, and gradient descent approach to predict the weather based on a few variables. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

“…ANN based STLF models commonly use back-propagation algorithm, which generally exhibits a slow and improper convergence that affects the forecast accuracy. …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. …”
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Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model by Yaseen, Z.M., Ebtehaj, I., Bonakdari, H., Deo, R.C., Danandeh Mehr, A., Mohtar, W.H.M.W., Diop, L., El-Shafie, A., Singh, V.P.

“…The present results have wider implications not only for streamflow forecasting purposes, but also for other hydro-meteorological forecasting variables requiring only the historical data input data, and attaining a greater level of predictive accuracy with the incorporation of the FFA algorithm as an optimization tool in an ANFIS model.…”
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A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST by BAHARUDIN, ZUHAIRI

“…The majority of the single variable based techniques are using autoregressive-moving average (ARMA) model to solve the STLF problem. …”
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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River by Katipo?lu O.M., Kartal V., Pande C.B.

“…The hybrid model is a novel approach for estimating sediment load based on various input variables. …”