Search Results - (( developing forecasting moving algorithm ) OR ( java implication force algorithm ))

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

“…Even though fuzzy time series (FTS) algorithm is able to overcome moving holiday electricity load demand (MH-ELD) forecasting problem, the current FTS algorithm lacks final model interpretation, is less interpretability of fuzzy logical relationship strength, and does not provide a complete FTS forecasting process. …”
Get full text
Get full text

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

“…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. …”
Get full text
Get full text
Get full text

Automated time series forecasting by Ismail, Suzilah, Zakaria, Rohaiza, Tuan Muda, Tuan Zalizam

“…Moving Average, Decomposition, Exponential Smoothing, Time Series Regressions and ARIMA) were used.The algorithm was developed in JAVA using up to date forecasting process such as data partition, several error measures and rolling process.Successfully, the results of the algorithm tally with the results of SPSS and Excel.This automatic forecasting will not just benefit forecaster but also end users who do not have in depth knowledge about forecasting techniques.…”
Get full text
Get full text
Get full text

The forecasting of poverty using the ensemble learning classification methods by Zamzuri, Muhammad Haziq Adli, Nadilah, Sofian, Hassan, Raini

“…This research was conducted to forecast poverty using classification methods. Random Forest and Extreme Gradient Boosting (XGBoost) algorithms were applied to forecast poverty since they are supervised learning algorithms that use the ensemble learning approach for classification. …”
Get full text
Get full text
Get full text

Forecasting time series data using hybrid grey relational artificial neural network and auto regressive integrated moving average model by Salleh, Roselina, Shamsuddin, Siti Mariyam

Get full text
Get full text

Performance of various forecasting algorithms to reduce the number of transmitted packets by sensor node in wireless sensor networks by Husni, Muhammed Ihsan

“…The simulation experiments are done using MATLAB software for a variety of algorithms. The selected algorithms for the reduction algorithm in the transmissions include Move Average algorithm (MA), Autoregressive all-pole model parameters — Burg’s algorithm. …”
Get full text
Get full text
Get full text
Get full text

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.…”
Get full text
Get full text
Get full text

A new hybrid genetic algorithm-sarima-artificial neural network in forecasting Malaysian export amount of palm oil by Chai, Kah Chun

“…Malaysia is a significant export country of palm oil to all over the world. Therefore, forecasting of palm oil export is required to help in boosting the nation’s socioeconomic development as well as for the plantation companies to sustain and improve for a better management regarding export. …”
Get full text
Get full text
Get full text
Get full text

New Approach for E-Commerce Stock Prices Prediction : Combination of Machine Learning and Technical Analysis by Kelvin Lee, Yong Ming, Mohamad, Jais, Pick Soon, Lee

“…This study aimed to develop a new approach for forecasting the price movements of e-commerce stocks. …”
Get full text
Get full text
Get full text
Get full text

Determining the order of a moving average model of time series using reversible jump MCMC: a comparison between laplacian and gaussian noises by Suparman, Suparman, Abdellah Salhi, Abdellah Salhi, Rusiman, Mohd Saifullah

“…Moving average (MA) is a time series model often used for pattern forecasting and recognition. …”
Get full text
Get full text
Get full text

Forecasting road deaths in Malaysia using support vector machine by Nurul Qastalani, Radzuan, Mohd Hasnun, Arif Hassan, Anwar P.P., Abdul Majeed, Rabiu Muazu, Musa, Khairil Anwar, Abu Kassim

“…This study proposes a new approach in forecasting the road deaths, by means of a machine learning algorithm known as Support Vector Machine. …”
Get full text
Get full text

Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting by Loh, Eng Chuen

Get full text
Get full text
Get full text
Get full text

Application of Evolutionary Algorithm for Assisted History Matching by Zahari, Muhammad Izzat

“…Besides, algorithm based method has been widely used to forecast future result in various field for example art, biology, marketing including engineering. …”
Get full text
Get full text

Flood forecasting for Melaka using arima and nar modelling methods by Wong, Wei Ming

“…A flash flood is challenging to forecast and requires a sophisticated algorithm and system compared to the seasonal flood. …”
Get full text
Get full text
Get full text
Get full text

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction by Heng S.Y., Ridwan W.M., Kumar P., Ahmed A.N., Fai C.M., Birima A.H., El-Shafie A.

Load demand analysis for high rise residentals of Petaling Jaya by Ng, Pik San

Rabies Outbreak Prediction Using Deep Learning with Long Short-Term Memory by Abdulrazak Yahya, Saleh, Shahrulnizam, Medang, Ashraf, Osman Ibrahim

“…As such, biosurveillance system developers are looking for highly sensitive outbreak prediction algorithms that will minimise the number of false alarms. …”
Get full text
Get full text
Get full text

Forecasting number of vulnerabilities using long short-term neural memory network by Hoque M.S., Jamil N., Amin N., Rahim A.A.A., Jidin R.B.

“…Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. …”

Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China by Sattar Hanoon M., Najah Ahmed A., Razzaq A., Oudah A.Y., Alkhayyat A., Feng Huang Y., kumar P., El-Shafie A.

Rainfall time series modeling for a mountainous region in West Iran by Mekanik, Fatemeh

“…A feedforward Artificial Neural Network (ANN) rainfall model and a Seasonal Autoregressive Integrated Moving Average (SARIMA) rainfall model were developed to investigate their potentials in forecasting rainfall. …”
Get full text
Get full text