# Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

arXiv: https://arxiv.org/abs/1908.08389 · HTML: https://mlsanity.com/paper/1908.08389/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [q-RBFNN:A Quantum Calculus-based RBF Neural Network](https://mlsanity.com/paper/2106.01370/) (2021)

## Abstract (arXiv)

Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extension of RBF neural networks for the prediction of chaotic time series. The proposed algorithm utilizes the concept of time-space orthogonality and separately deals with the temporal dynamics and spatial non-linearity(complexity) of the chaotic series. The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF. The spatio-temporal RBF is shown to out perform the standard RBFNN by achieving significantly reduced estimation error.