PMANet：中国股票价格预测的预测模型
案例介绍
approach involves taking input time series in two dimensions—time step and feature—and performing multi-scale
convolution and gated convolution operations. This methodology allows for a more thorough extraction of stock price
information. • The APSO is a novel approach that merges the Ant Colony Algorithm (ACO) with the Particle Swarm Algorithm (PSO), leveraging both global and local search characteristics. This hybrid method enables the network to identify optimal
solutions more rapidly and accurately, especially in handling mutation points present in the data
PMANet：中国股票价格预测的预测模型
案例介绍
We introduce MTFC to improve the network’s capability in processing complex features of long sequences. This
approach involves taking input time series in two dimensions—time step and feature—and performing multi-scale
convolution and gated convolution operations. This methodology allows for a more thorough extraction of stock price
information. • The APSO is a novel approach that merges the Ant Colony Algorithm (ACO) with the Particle Swarm Algorithm (PSO), leveraging both global and local search characteristics. This hybrid method enables the network to identify optimal
solutions more rapidly and accurately, especially in handling mutation points present in the data
案例图片
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