Generalized space time autoregressive (gstar)-artificial neural network (ann) model with multilayer feedforward networks architecture

1st International Conference on Environmental Geography and Geography Education

生态环境科学;地球科学

Saputro, D.R.S.^1 ; Putri, I.M.^1 ; Sutanto^1 ; Noor, N.H.^1 ; Widyaningsih, P.^1

Universitas Sebelas Maret, Solo, Indonesia^1

关键词: Artificial neural network models;    Auto-regressive;    Functional forms;    Input neurons;    Multilayer feedforward networks;    Nonlinear pattern;    Time-series data;    Training process;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/243/1/012039/pdf
DOI  :  10.1088/1755-1315/243/1/012039

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The GSTAR model is one linear space time model used to model time series data with inter-location linkages. One of the weaknesses of the GSTAR model is that the model has not been able to capture any nonlinear patterns that may arise. The ANN is one model of nonlinear artificial intelligence that has a flexible functional form and is a supervised engine learning that provides a good framework to represent a relationship in time series data. Due to the advantages of such ANN, it can be combined with GSTAR model. In this research conducted study of GSTAR-ANN model and its network architecture. The model is constructed in 3 layers namely input, hidden and output. The GSTAR model is used as input in the training process. Each input will receive an input signal and forward the signal to the hidden layer, then to the output layer. The GSTAR-ANN model has network architecture with one neuron unit at the output layer, p input neuron, q neuron in hidden layer (multilayer feedforward networks).