Abstract
With the rapid development of e-commerce (EC) and shopping online, accurate and efficient forecasting of e-commerce sales (ECS) is very important for making strategies for purchasing and inventory of EC enterprises. Affected by many factors, ECS volume range varies greatly and has both linear and nonlinear characteristics. Three forecast models of ECS, autoregressive integrated moving average (ARIMA), nonlinear autoregressive neural network (NARNN), and ARIMA-NARNN, are used to verify the forecasting efficiency of the methods. Several time series of ECS from China’s Jingdong Corporation are selected as experimental data. The result shows that the ARIMA-NARNN model is more effective than ARIMA and NARNN models in forecasting ECS. The analysis found that the ARIMA-NARNN model combines the linear fitting of ARIMA and the nonlinear mapping of NARNN, so it shows better prediction performance than the ARIMA and NARNN methods.
Highlights
In recent years, e-commerce has developed rapidly in China
There is a dramatic fluctuation in e-commerce sales (ECS) volume, and the ECS shows a linear trend of increase or decrease in a specific period of time, but certain phases may show the characteristics of nonlinear fluctuation because of various potential uncertainties
We choose the e-commerce sales time series of many single products from Jingdong Company in China as empirical analysis data sets and forecast the time series of weekly sales by ECS-autoregressive integrated moving average (ARIMA) model
Summary
E-commerce has developed rapidly in China. The forecasting of ECS greatly affects inventory, ordering, and logistics strategies, so it is very important for e-commerce enterprises to predict the ECS accurately [1]. ARIMA, NARNN, and ARIMA-NARNN have been studied in many industries, such as agriculture and forestry [2], healthcare [3, 5], geography [4], manufacturing [6], and offline retail [7] Some of these studies [2,3,4,5] only analyzed a single time series to reach conclusions, and some [6, 7] only conducted empirical analysis of the hybrid model and did not compare the ARIMA, NARNN, and ARIMANARNN to prove the effectiveness of the hybrid model.
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