Economic forecasting based on neural network with weight learning and local connection

Abstract

Machine learning, as the core of artificial intelligence technology, has been rapidly developed in recent years, and has made breakthrough progress in many fields. Similarly, machine learning has been widely used in the field of economic management. Unlike other fields, data in the economic field is often complex and disordered. This complexity and disorder limit the use of some machine learning methods, but it gives neural network a huge space to play. The largest advantage of neural network is that there is no requirement on the structure of the input data. However, previous work has applied neural networks directly, without making specific improvements based on the structure in economics. In the actual economic forecast and decision-making, although there are many influencing factors, the weight of each factor is not the same. Previous neural networks put all the data into the network and then got a result without considering the different weights of each factor. We propose a new neural network with different weights forecasting and local connections, which can apply different weights to each factor to get more accurate and practical results. We use our proposed method to forecast the sales volume of Haier company, and the results show that our method is significantly better than the previous method.