Evolving Hybrid Generalized Space-Time Autoregressive Forecasting with Cascade Neural Network Particle Swarm Optimization

Abstract

Background: The generalized space-time autoregressive (GSTAR) model is one of the most widely used models for modeling and forecasting time series and location data. Methods: In the GSTAR model, there is an assumption that the research locations are heterogeneous. In addition, the differences between these locations are shown in the form of a weighting matrix. The novelty of this paper is that we propose the hybrid time-series model of GSTAR uses the cascade neural network and obtains the best parameters from particle swarm optimization. Results and conclusion: This hybrid model provides a high accuracy value for forecasting PM2.5, PM10, NOx, and SO2 with high accuracy forecasting, which is justified by a mean absolute percentage error (MAPE) accuracy of around 0.01%.

Space-time autoregressive Hilbertian models andtheir application for wind speed

“…Following the same idea as in , the weighting matrices are considered to be the same in autoregressive and moving average parts. This model has found numerous applications in environmental ( [6]; [9]; [10]; [11]; [12]), epidemiological ([4]; [13]), economical ( [14]; [15]) and criminology( [16])studies.…”

Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting

“…These two models are being developed both independently and in hybrid forms to analyze climate data. To forecast air quality and pollution, GSTAR has been integrated with the NN model [28], and the Generalized Regression Neural Network (GRNN) model has been employed to predict solar radiation, enabling the identification of outlier data [29]. Nonlinear patterns can be generated and effectively addressed using spatio-temporal techniques in conjunction with NNs [30].…”