Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting
Intelligent financial forecasting modeling plays an important role in facilitating investment-
related decision-making activities in financial markets. However, accurate multivariate …
related decision-making activities in financial markets. However, accurate multivariate …
Gated spiking neural P systems for time series forecasting
Q Liu, L Long, H Peng, J Wang, Q Yang… - … on Neural Networks …, 2021 - ieeexplore.ieee.org
Spiking neural P (SNP) systems are a class of neural-like computing models, abstracted by
the mechanism of spiking neurons. This article proposes a new variant of SNP systems …
the mechanism of spiking neurons. This article proposes a new variant of SNP systems …
High-efficiency chaotic time series prediction based on time convolution neural network
W Cheng, Y Wang, Z Peng, X Ren, Y Shuai… - Chaos, Solitons & …, 2021 - Elsevier
The prediction of chaotic time series is important for both science and technology. In recent
years, this type of prediction has improved significantly with the development of deep …
years, this type of prediction has improved significantly with the development of deep …
Nonlinear spiking neural systems with autapses for predicting chaotic time series
Q Liu, H Peng, L Long, J Wang, Q Yang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing
models that are inspired by the mechanism of spiking neurons and are 3rd-generation …
models that are inspired by the mechanism of spiking neurons and are 3rd-generation …
A real-time collision prediction mechanism with deep learning for intelligent transportation system
Rear-end collision prediction has gained an increasing attention for safety improvement in
smart cities. It is urgent to design efficient warning strategies for rear-end collisions which is …
smart cities. It is urgent to design efficient warning strategies for rear-end collisions which is …
Structured manifold broad learning system: A manifold perspective for large-scale chaotic time series analysis and prediction
High-dimensional and large-scale time series processing has aroused considerable
research interests during decades. It is difficult for traditional methods to reveal the evolution …
research interests during decades. It is difficult for traditional methods to reveal the evolution …
An accurate GRU-based power time-series prediction approach with selective state updating and stochastic optimization
Accurate power time-series prediction is an important application for building new
industrialized smart cities. The gated recurrent units (GRUs) models have been successfully …
industrialized smart cities. The gated recurrent units (GRUs) models have been successfully …
混沌时间序列分析与预测研究综述.
韩敏， 任伟杰， 李柏松， 冯守渤 - Information & Control, 2020 - search.ebscohost.com
摘要复杂系统产生的混沌时间序列普遍存在于天文, 水文, 气象, 环境, 金融等领域.
混沌时间序列的分析与预测对于理解复杂系统特性, 探究系统演化规律具有重要作用 …
混沌时间序列的分析与预测对于理解复杂系统特性, 探究系统演化规律具有重要作用 …
A neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints
N Liu, S Qin - Neural Networks, 2019 - Elsevier
This paper presents a neurodynamic approach to nonlinear optimization problems with
affine equality and convex inequality constraints. The proposed neural network endows with …
affine equality and convex inequality constraints. The proposed neural network endows with …
A smart cloud and IoVT-based kernel adaptive filtering framework for parking prediction
Smart vehicle parking is a collaborative effort of technology and human innovation where
the efforts are to be minimized to save time and efforts. In smart cities it is one of the common …
the efforts are to be minimized to save time and efforts. In smart cities it is one of the common …