Results 91 to 100 of about 72,436 (339)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Prediksi Harga Saham PT. Bri, Tbk. Menggunakan Metode Arima (Autoregressive Integrated Moving Average) [PDF]

, 2014
PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average) Greis S. Lilipaly1) , Djoni Hatidja1) , John S.
Hatidja, D. (Djoni), Kekenusa, J. S. (John), Lilipaly, G. S. (Greis)   +2 more
core  

The Comparative Analysis of Integrated Moving Average and Autoregressive Integrated Moving Average Methods for Predicting Bitcoin Returns

Journal of Mathematics, Computations and Statistics
The rise in popularity of cryptocurrencies such as Bitcoin across various platforms has attracted the attention of young investors, making it easier for them to invest. However, due to the volatile nature of Bitcoin, this type of investment carries a high risk.
null Brigita Tiara Elgityana Melantika, null Kalfin, Bakti Siregar, null Wiwik Wiyanti   +3 more
openaire   +1 more source

The Application Of Autoregressive Integrated Moving Average Generalized Autoregressive Conditional Heteroscedastic (Arima - Garch)

Udayana Journal of Social Sciences and Humanities (UJoSSH), 2020
Rice farming for Indonesia is vital. Rice farming is inseparable from the fact that rice farming is the livelihood of most of the population, while rice is the staple food of almost all Indonesians. The nature of rice that is easy to process and, following the public consumption culture, causes a very high dependence on rice.
Bekti Endar Susilowati, Sarah Sholikhatun Risma, Oki Prasetia Hendarsin, Pardomuan Robinson Sihombing   +3 more
openaire   +2 more sources

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

A fractionally integrated autoregressive moving average approach to forecasting tourism demand

Tourism Management, 2008
The primary aim of this paper is to incorporate fractionally integrated ARMA (p, d, q) (ARFIMA) models into tourism forecasting, and to compare the accuracy of forecasts with those obtained by previous studies. The models are estimated using the volume of monthly international tourist arrivals in Singapore.
openaire   +3 more sources

Computational Models of Multisensory Integration with Recurrent Neural Networks: A Critical Review and Future Directions

Advanced Intelligent Systems, EarlyView.
This review outlines how recurrent neural networks model multisensory integration by capturing temporal and probabilistic features of sensory input. Key developments, challenges, and future directions are summarized, providing insights into biologically inspired AI. Multisensory integration (MSI) is a core brain function underlying perception, learning,
Ehsan Bolhasani, Seyed Hamed Aboutalebi, Yaser Merrikhi   +2 more
wiley   +1 more source

Bio‐to‐Robot Transfer of Fish Sensorimotor Dynamics via Interpretable Model

Advanced Intelligent Systems, EarlyView.
This study demonstrates how a biologically interpretable model trained on real‐fish muscle activity can accurately predict the motion of a robotic fish. By linking real‐fish sensorimotor dynamics with robotic fish, the work offers a transparent, data‐efficient framework for transferring biological intelligence to bioinspired robotic systems.
Waqar Hussain Afridi, Ahsan Tanveer, Rahdar Hussain Afridi, Muhammad Hamza, Mingxin Wu, Liang Li, Guangming Xie   +6 more
wiley   +1 more source

A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study

International Journal of Engineering Business Management, 2009
Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as ...
C. A. Mitrea, C. K. M. Lee, Z. Wu
doaj   +2 more sources