Results 41 to 50 of about 11,326 (149)

Transforming oil market analysis: A novel GAN + LSTM predictive framework

Next Energy
A novel method of predicting the crude oil WTI futures prices based on a data set covering April 12, 2009 through January 7, 2024. To capture complex market dynamics more precisely, it incorporates key market factors such as open, high, and low price ...
Prity Kumari, G.Y. Chandan, Satish Kumar M   +2 more
doaj   +1 more source

Parameter identification of unmanned surface vehicle MMG model based on an improved extended Kalman filter

Zhongguo Jianchuan Yanjiu
ObjectivesTo construct an accurate MMG （mathematical model group） model for a water-jet propulsion unmanned surface vehicle, the traditional extended Kalman filter algorithm and improved extended Kalman filter algorithm are combined with the real-world ...
Pengbo SUN, Zaopeng DONG, Wei LIU, Jinliang SHENG, Zhihao LI   +4 more
doaj   +1 more source

KAJIAN MODEL HIDDEN MARKOV UNTUK MENDUGA VOLATILITAS INDEKS HARGA SAHAM

Prima: Jurnal Pendidikan Matematika, 2019
Abstrak Volatility is a measure of uncertainty. Volatility can either be measured by using the standard deviation or variance between returns. The problem is volatility is unobservable, and estimating volatility is not a trivial task.
Abdul Baist
doaj   +1 more source

Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China

BMC Infectious Diseases, 2023
Background This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network
Hansong Zhu, Si Chen, Rui Liang, Yulin Feng, Aynur Joldosh, Zhonghang Xie, Guangmin Chen, Lingfang Li, Kaizhi Chen, Yuanyuan Fang, Jianming Ou   +10 more
doaj   +1 more source

Machine Learning-Driven Demand Forecasting in the Automotive Supply Chain

Applied Computational Intelligence and Soft Computing
Demand forecasting is a critical component and a major challenge within supply chain management. Accurate forecasts can help reduce costs, improve operational efficiency, and increase overall company performance.
Abdellah Bais, Khalid Amechnoue
doaj   +1 more source

Utvärdera maskininlärningsmodeller för tidsserieprognos inom smarta byggnader

, 2023
Temperature regulation in buildings can be tricky and expensive. A common problem when heating buildings is that an unnecessary amount of energy is supplied. This waste of energy is often caused by a faulty regulation system.
Balachandran, Sarugan, Perez Legrand, Diego   +1 more
core   +1 more source

Bridge Dynamic Strain Prediction Based on Stacked GRU Neural Network [PDF]

Jisuanji gongcheng
As important infrastructures, bridges may face considerable safety hazards due to the long-term influence of the natural environment and daily loads. Therefore, the health status of bridge structures must be monitored and predicted in real time.
LIU Xiaoyu, LIAO Zhifang, TAN Sui, YU Zhiwu
doaj   +1 more source

Earthquake magnitude prediction in Indonesia using a supervised method based on cloud radon data [PDF]

In the challenging realm of earthquake prediction, the reliability of forecasting systems has remained a persistent obstacle. This study focuses on earthquake magnitude prediction in Indonesia, leveraging supervised machine learning techniques and cloud ...
Sunarno, Sunarno, Wijatna, Agus Budhie, Haryono, Eko, Pratama, Thomas Oka   +3 more
core   +1 more source

Macroeconomic-aware forecasting of construction costs in developing countries: Using gated recurrent unit and long short-term memory deep learning framework.

PLoS ONE
Cost overruns are common on long-term construction projects. This is mostly because of inaccurate early estimates and unexpected changes in the economy and finances.
Majed Alzara, Nadeen Gihad, Heba Abdou, Akram Soltan, AlHussein Hilal, Ahmed Ehab   +5 more
doaj   +1 more source

FORECASTING CRUDE OIL MARKET VOLATILITY: TEST OF SYMMETRIC AND ASYMMETRIC GARCH–TYPE MODELS [PDF]

, 2006
The purpose of this thesis is to compare the predictive power of three different volatility forecasting models on Brent Crude Oil Index data under two different market conditions. The models included are GARCH, TARCH, and EGARCH.
Heikkilä, Juha
core