Deep State Space Recurrent Neural Networks for Time Series Forecasting

Hugo Inzirillo

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arXiv · 2407.15236

Deep State Space Recurrent Neural Networks for Time Series Forecasting

Hugo Inzirillo

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Abstract

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.

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BibTeX RIS

Hugo Inzirillo. 2024-07-21. Deep State Space Recurrent Neural Networks for Time Series Forecasting. https://arxiv.org/abs/2407.15236

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