深度状态空间循环神经网络用于时间序列预测
Deep State Space Recurrent Neural Networks for Time Series Forecasting
AI总结：
针对加密货币市场动态预测，提出融合状态空间模型与RNN的框架，采用LSTM、GRU和TKANs，其中TKANs表现最佳。
摘要：
我们探索了多种神经网络架构来建模加密货币市场的动态。传统的线性模型往往无法准确捕捉该市场独特而复杂的动态。相比之下，深度神经网络（DNNs）在时间序列预测中已展现出相当的能力。本文提出了一种新颖的神经网络框架，将计量经济学状态空间模型的原理与循环神经网络（RNNs）的动态能力相结合。我们提出了使用长短期记忆（LSTM）、门控残差单元（GRU）和时序Kolmogorov-Arnold网络（TKANs）的状态空间模型。根据结果，受Kolmogorov-Arnold网络（KANs）和LSTM启发的TKANs展现出有前景的结果。
英文摘要：
We explore various neural network architectures for modeling the dynamics of the cryptocurrency market. Traditional linear models often fall short in accurately capturing the unique and complex dynamics of this market. In contrast, Deep Neural Networks (DNNs) have demonstrated considerable proficiency in time series forecasting. This papers introduces novel neural network framework that blend the principles of econometric state space models with the dynamic capabilities of Recurrent Neural Networks (RNNs). We propose state space models using Long Short Term Memory (LSTM), Gated Residual Units (GRU) and Temporal Kolmogorov-Arnold Networks (TKANs). According to the results, TKANs, inspired by Kolmogorov-Arnold Networks (KANs) and LSTM, demonstrate promising outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
