Results 91 to 100 of about 73,625 (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

Asymptotic properties of autoregressive integrated moving average processes

Stochastic Processes and their Applications, 1975
AbstractIn this paper we study the asymptotic behavior of so-called autoregressive integrated moving average processes. These processes constitute a large class of stochastic difference equations which includes among many other well-known processes the simple one-dimensional random walk. They were dubbed by G.E.P. Box and G.M. Jenkins who found them to
openaire   +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  

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

Parameters Estimate of Autoregressive Moving Average and Autoregressive Integrated Moving Average Models and Compare Their Ability for Inflow Forecasting [PDF]

Journal of Mathematics and Statistics, 2012
In this study the ability of Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models in forecasting the monthly inflow of Dez dam reservoir located in Teleh Zang station in Dez dam upstream i s estimated. ARIMA model has found a widespread application in many practical sciences. In addition, dam reservoir inflow
openaire   +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

Mother–Child Biobehavioral Synchrony and Its Association With Social Functioning in Autistic School‐Aged Children

Autism Research, EarlyView.
ABSTRACT Parent–child biobehavioral synchrony, or the concordance of behavior and physiological indicators between individuals, is theorized to support children's social development; however, this relationship has yet to be investigated in autistic children.
Carly Moser, Chih‐Hsiang Yang, Abigail L. Hogan, Amanda Fairchild, Jane Roberts, Jessica Klusek   +5 more
wiley   +1 more source

Generative Deep Learning for Advanced Battery Materials

Batteries &Supercaps, EarlyView.
This review explores the role of generative deep learning (DL) in battery materials analysis and highlights the fundamental principles of generative DL and its applications in designing battery materials. The importance of using multimodal data is underscored to effectively address the challenges faced during the development of battery materials across
Deepalaxmi Rajagopal, Adrian Cierpka, Britta Nestler, Arnd Koeppe   +3 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