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Neural Networks Ensemble: Evaluation of Aggregation Algorithms for Forecasting by HASSAN, SAIMA

“…The performances ofthese aggregation algorithms ofNNs ensemble were evaluated with the mean absolutepercentage error and symmetric mean absolute percentage error. …”
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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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Investigating photovoltaic solar power output forecasting using machine learning algorithms by Essam Y., Ahmed A.N., Ramli R., Chau K.-W., Idris Ibrahim M.S., Sherif M., Sefelnasr A., El-Shafie A.

“…To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. …”

M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589) by Mansor, Rosnalini, Mat Kasim, Maznah, Othman, Mahmod, Zaini, Bahtiar Jamili

“…Hence, the WeSuSFTS algorithm succeeds to improve the MH-ELD forecasting accuracy.…”
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A new soft computing model for daily streamflow forecasting by Sammen S.S., Ehteram M., Abba S.I., Abdulkadir R.A., Ahmed A.N., El-Shafie A.

Weighted subsethood and reasoning based fuzzy time series for moving holiday electricity load demand forecasting by Rosnalini, Mansor

“…Besides, different characteristics of each moving holiday and existence of a great number of irregularities in the load data also contribute to the forecasting inaccuracy and uncertainty. Fuzzy time series (FTS) algorithm is able to overcome moving holiday electricity load demand (MH-ELD) forecasting problem, but the FTS algorithm lacks final model interpretation, less interpretability of fuzzy logical relationship strength, and does not provide a complete FTS forecasting process. …”
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Weather prediction system using ANN algorithm / Nur Afiqah Ahmad Sukri by Ahmad Sukri, Nur Afiqah

“…Overall, this study advances the science of weather forecasting by showing how well ANN algorithms can capture intricate weather patterns.…”
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CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION by Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Khairul Anwar, Mohamad Said

“…Future precipitation forecasts revealed that the city would experience an increase in mean monthly precipitation of 2%~26% in the 2030s, 0%~34% in the 2050s, and 4%~43% in the 2080s during wet seasons, relative to the 1970s. …”
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A new hybrid genetic algorithm-sarima-artificial neural network in forecasting Malaysian export amount of palm oil by Chai, Kah Chun

“…The performance of the proposed hybrid GASARIMA-ANN was compared with four existing models, which were SARIMA, ANN, hybrid GA-SARIMA, and hybrid SARIMA-ANN. The forecast accuracy for all the models was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Pearson correlation coefficient. …”
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Empirical analysis of parallel-NARX recurrent network for long-term chaotic financial forecasting by Abdulkadir, S.J., Yong, S.-P.

“…The experimental results based on mean absolute percentage error (MAPE) and other forecasting error metrics shows that P-NARX network trained with Bayesian regulation slightly outperforms Levenberg-marquardt, Resilient back-propagation and one-step-secant training algorithm in forecasting daily Kuala Lumpur Composite Indices. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…Given the multitude of components to manage, streamflow forecasting is preferable to employ an algorithm with low sensitivity to parameter variations. …”
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A Case Study of Using Long Short-Term Memory (LSTM) Algorithm in Solar Photovoltaic Power Forecasting by Lee Chin, Kho, Sze Song, Ngu, Annie, Joseph, Siti Kudnie, Sahari, Kuryati, Kipli, R., Rulaningtyas

“…The results show that the deep learning algorithm provides reliable forecasting results.…”
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Reservoir water balance simulation model utilizing machine learning algorithm by Dashti Latif S., Najah Ahmed A., Sherif M., Sefelnasr A., El-Shafie A.

Optimal short term load forecasting using LSSVM and improved BFOA considering Malaysia pandemic disrupted situation by Zaini, Farah Anishah

“…Due to that reason, in this study, the hybrid forecasting model based on the Least Square Support Vector Machine (LSSVM) and Improved Bacterial Foraging Optimization Algorithm (IBFOA) is developed to perform an accurate STLF and applied to load in Peninsular Malaysia during the pandemic disrupted situation. …”
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Air quality forecasting and mapping in Malaysian urban areas: A hybrid deep learning approach by Nur'atiah Zaini

Forecasting sunspot numbers with Feedforward Neural Networks (FNN) using 'sunspot neural forecaster' system by Reza Ezuan, Samin, Muhammad Salihin, Saealal, Azme, Khamis, Syahirbanun, Isa, Ruhaila, Md. Kasmani

“…Simulations are done using Matlab 7 where customized Graphic User Interface (GUI) called `Sunspot Neural Forecaster' have been developed for analysis. A complete analysis for different learning algorithms, sunspot data models and FNN transfer functions are examined in terms of Mean Square Error (MSE) and correlation analysis. …”
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NEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM by Razak I.A.W.A., Abidin I.Z., Siah Y.K., Sulaima M.F.

“…This is due to the fact that only two algorithms were used (LSSVM and GA), with the load and HOEP for the week preceding the forecasting hour as the inputs. …”

Daily Rainfall Forecasting Using Meteorology Data with Long Short-Term Memory (LSTM) Network by Soo See, Chai, Goh, Kok Luong

“…Rainfall is a natural climatic phenomenon and prediction of its value is crucial for weather forecasting. For time series data forecasting, the Long Short-Term Memory (LSTM) network is shown to be superior as compared to other machine learning algorithms. …”
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Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy by Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., El-Shafie A.

Forecasting the air pollution index using artifical neural network at Muar, Johor, Malaysia / Ahmad Farid Rasdi by Rasdi, Ahmad Farid

“…It is shown that ANN was more accurately to be used as a forecasting method and to improve the accuracy of the forecasting compare to Naïve, Mean and ARIMA model using the lowest measures error which are Mean Error (ME), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE). …”
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