Results 31 to 40 of about 10,511 (262)

Fisher’s z Distribution-Based Mixture Autoregressive Model

Econometrics, 2021
We generalize the Gaussian Mixture Autoregressive (GMAR) model to the Fisher’s z Mixture Autoregressive (ZMAR) model for modeling nonlinear time series.
Arifatus Solikhah, Heri Kuswanto, Nur Iriawan, Kartika Fithriasari   +3 more
doaj   +1 more source

Analysis of Oil Price Effect on Economic Growth of ASEAN Net Oil Exporters

Energies, 2019
In this paper, the linear and nonlinear effects of oil price on growth for Association of Southeast Asian Nations (ASEAN)—3 net oil-exporting countries, namely Brunei, Malaysia and Vietnam, are investigated.
Karunanithi Kriskkumar, Niaz Ahmad Mohd Naseem   +1 more
doaj   +1 more source

Forecasting performance of smooth transition autoregressive (STAR) model on travel and leisure stock index

Journal of Finance and Data Science, 2018
Travel and leisure recorded a consecutive robust growth and become among the fastest economic sectors in the world. Various forecasting models are proposed by researchers that serve as an early recommendation for investors and policy makers.
Usman M. Umer, Tuba Sevil, Güven Sevil
doaj   +1 more source

Improving Quality of Long-Term Bond Price Prediction Using Artificial Neural Networks

Kvalita Inovácia Prosperita, 2021
Purpose: The aim of this paper is to propose nonlinear autoregressive neural network which can improve quality of bond price forecasting.         Methodology/Approach: Due to the complex nature of market information that influence bonds, artificial ...
Robert Verner, Michal Tkáč, Michal Tkáč   +2 more
doaj   +1 more source

Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition

Journal of Applied Mathematics, 2013
An input nonlinear system is decomposed into two subsystems, one including the parameters of the system model and the other including the parameters of the noise model, and a multi-innovation stochastic gradient algorithm is presented for Hammerstein ...
Huiyi Hu, Xiao Yongsong, Rui Ding
doaj   +1 more source

IDENTIFIKASI MODEL SELF-EXCITING THRESHOLD AUTOREGRESSIVE DENGAN SWITCHING TWO REGIME (KASUS PADA DATA EKSPOR AGRIKULTUR DI INDONESIA)

Barekeng, 2020
A time series model that explain the structural changes associated with data in a certain time period is the Threshold Autoregressive (TAR) model. The basic of the TAR model there are some different usage regimes in autoregressive analysis.
Husnun Nur Ghiffari Putri Riyansyah, Dewi Retno Sari Saputro, Bowo Winarno   +2 more
doaj   +1 more source

Resistance in a non-linear autoregressive model of pulmonary mechanics

Current Directions in Biomedical Engineering, 2016
Respiratory system modelling can enable patient-specific mechanical ventilator settings to be found, and can thus reduce the incidence of ventilator induced lung injury in the intensive care unit.
Langdon Ruby, Docherty Paul D., Laufer Bernhard, Möller Knut   +3 more
doaj   +1 more source

Learned Conformational Space and Pharmacophore Into Molecular Foundational Model

Advanced Science, EarlyView.
The Ouroboros model introduces two orthogonal modules within a unified framework that independently learn molecular representations and generate chemical structures. This design enables flexible optimization strategies for each module and faithful structure reconstruction without prompts or noise.
Lin Wang, Yifan Wu, Hao Luo, Minglong Liang, Yihang Zhou, Cheng Chen, Chris Liu, Jun Zhang, Yang Zhang   +8 more
wiley   +1 more source

Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain

Advanced Science, EarlyView.
A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio, Tommaso Gili, Andrea Gabrielli, Maurizio Mattia   +3 more
wiley   +1 more source

In Situ Quantization with Memory‐Transistor Transfer Unit Based on Electrochemical Random‐Access Memory for Edge Applications

Advanced Science, EarlyView.
By combining ionic nonvolatile memories and transistors, this work proposes a compact synaptic unit to enable low‐precision neural network training. The design supports in situ weight quantization without extra programming and achieves accuracy comparable to ideal methods. This work obtains energy consumption advantage of 25.51× (ECRAM) and 4.84× (RRAM)
Zhen Yang, Yuxiang Yang, Baiqian Wang, Yaoyu Tao, Zelun Pan, Lei Cai, Teng Zhang, Longhao Yan, Xianbin Li, Yuchao Yang   +9 more
wiley   +1 more source