Adaptive Spatiotemporal Neural Models for Time Series Prediction

Abstract

This paper introduces the Adaptive Spatiotemporal Neural Models (ASPNM), a novel approach to time series prediction that addresses the limitations of traditional, static neural network models. The core idea is to design a neural network architecture capable of dynamically adjusting its internal parameters and behavioral patterns based on the characteristics of the input time series and its historical information. This adaptation is achieved through a combined mechanism utilizing Recurrent Neural Networks (RNNs) for state representation, reinforcement learning for parameter optimization, and genetic algorithms for behavioral pattern refinement. The resulting ASPNM models demonstrate improved prediction accuracy compared to conventional models, particularly when dealing with complex and non-stationary time series data. The key innovation lies in the model's ability to learn and adapt, mimicking the dynamic nature of real-world time series phenomena. The models are evaluated using various benchmark datasets and demonstrate superior performance across diverse scenarios. This work presents a promising direction for enhancing time series prediction capabilities.