A hybrid attention and time series network for enterprise sales forecasting under digital management and edge computing

Abstract

AbstractEnterprises have both new opportunities and new challenges as a result of the rapid advancements in information technology that have accompanied the age of economic globalization. With the growth of internet of Things devices, data sizes have significantly increased. Further, the traditional cloud platform has been enriched with edge computing so that the huge data can be processed where it is collected. Therefore, businesses must adapt to new size requirements and rising standards for technical content. Forecasting corporate sales has emerged as a hot topic in the field of digital management. To successfully direct the future production and existence of enterprises, time series forecasting is of utmost importance and value. This is because it makes use of already-existing data to get the best predicting result. This work proposes a combination of enterprise sales forecasting from the perspective of digital management and neural networks, and proposes a network HATT-CNN-BiLSTM model for enterprise sales forecasting. First, this work combines multi-scale CNN (MSCNN) with improved BiLSTM (IBiLSTM) model. The MSCNN is utilized to extract spatial features with different scale, and it is often impossible to effectively explore the rules of time series features, and the processing of time series data is the strength of the LSTM network. Moreover, the IBiLSTM model can explore time series features in both directions, and therefore more useful information can be obtained. The MSCNN-IBiLSTM model, which is composed of MSCNN and IBiLSTM, can take advantage of strengths and avoid weaknesses, and give full play to the roles of the two models in different fields. Second, this work proposes a hybrid attention mechanism that combines self-attention, channel attention, and spatial attention. It enhances features extracted by MSCNN-IBiLSTM through a hybrid attention to build HATT-MSCNN-IBiLSTM network, which can extract more discriminative features. Third, this work conducts comprehensive and systematic experiments on HATT- MSCNN-IBiLSTM to verify feasibility of the proposed method. The proposed model is implemented over an edge computing platform that increases the model training speed and improve the response time.