Discussing the article: "Neural Networks in Trading: Two-Dimensional Connection Space Models (Chimera)"

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Check out the new article: Neural Networks in Trading: Two-Dimensional Connection Space Models (Chimera).

In this article, we will explore the innovative Chimera framework: a two-dimensional state-space model that uses neural networks to analyze multivariate time series. This method offers high accuracy with low computational cost, outperforming traditional approaches and Transformer architectures.

Classical statistical methods require significant preprocessing of raw data and often fail to adequately capture complex nonlinear dependencies. Deep neural network architectures have demonstrated high expressiveness, but the quadratic computational complexity of Transformer-based models makes them difficult to apply to multivariate time series with a large number of features. Furthermore, such models often fail to distinguish seasonal and long-term components or rely on rigid a priori assumptions, limiting their adaptability in various practical scenarios.

One approach addressing these issues was proposed in the paper "Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models". The Chimera framework is a two-dimensional state space model (2D-SSM) that applies linear transformations both along the temporal axis and along the variable axis. Chimera comprises three primary components: state space models along the time dimension, along the variables dimension, and cross-dimensional transitions. Its parameterization is based on compact diagonal matrices, enabling it to replicate both classical statistical methods and modern SSM architectures.

Additionally, Chimera incorporates adaptive discretization to account for seasonal patterns and characteristics of dynamic systems.

Author: Dmitriy Gizlyk