Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

Abstract

GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.

Comparative Analysis of LSTM, ARIMA, and Hybrid models for Forecasting Future GDP

“…Comparison between the proposed approach and other scientific papers, such as those documented by Muchisha et al [8,20,25,23], reveals a broad application of machine-learning techniques and deep learning for GDP prediction. These studies reported accuracies of 91%, 80-90%, 98.71%, and 87%, respectively.…”

“…In the study by Hua [25], used the ARIMA and BPNN models to create a GDP forecasting model for the province of H. The results showed that the ARIMA model had a better long-term predictive effect than the improved combined model, and that the combined model outperformed a short-term prediction.…”

Hybrid Forecasting Methods—A Systematic Review

“…SARIMA is an extension of the ARIMA model, which also considers seasonality. In the fourth group of studies, models using neural networks more rarely implemented than LSTM were studied [7,8,[33][34][35]. In this group are works that combined ARIMA with back propaga-tion neural networks (BPNN) [33][34][35] or works that combined ARIMA with generalized regression neural networks (GRNN) [7,8].…”

“…In the fourth group of studies, models using neural networks more rarely implemented than LSTM were studied [7,8,[33][34][35]. In this group are works that combined ARIMA with back propaga-tion neural networks (BPNN) [33][34][35] or works that combined ARIMA with generalized regression neural networks (GRNN) [7,8]. In the last group are models that include wavelet transform decomposition that allows for the separation of linear and nonlinear parts of a temporal series a priori to the actual forecasting [8,[36][37][38].…”

Urban Green Space Planning and Design for Sponge City

“…When using ANN to simulate the existing urban green space system planning [26,27], the omnidirectional expression of the landscape index should be considered on the whole green space landscape as far as possible and the index change should be evaluated after the construction of the network from the following aspects:…”