FS-LSTM:sales forecasting in e-commerce on feature selection

There are many studies on sales forecasting in e-commerce,most of which focus on how to forecast sales volume with related e-commerce operation data.In this paper,a deep learning method named FS-LSTM was proposed,which combines long short-term memory(LSTM)and feature selection mechanism to forecast the sales volume.The indicators with most contributions by the extreme gradient boosting(XGBoost)model are selected as the input features of LSTM model.FS-LSTM method can get less mean average error(MAE)and mean squared error(MSE)in the forecasting of e-commerce sales volume,comparing with the LSTM model without feature selection.The results show that the FS-LSTM can improve the performance of original LSTM for forecasting the sales volume.

Comparison of Sales Prediction in Conventional Insights and Machine Learning Perspective

With the integration of global economy development and the rapid growth of science knowledge and technology,the needs of people’s consumption are increasingly personalized and diversified.Such a market background makes sales forecasting become an indispensable part of enterprise management and development.The definition of the sales forecasting is that based on the past few years’sales situation,the enterprises through systematic sales forecasting models estimate of the quantity and amount of all or some specific sales products and services in a specific time in the future.Accurate sales forecasting can promote enterprises to do better in future revenue,and can also encourage enterprises to set and keep an efficient sales management team.This paper will analyze traditional sales forecasting methods and sales forecasting methods based on big data models related to the perspective of machine learning,and then compare them.The research shows that the two sales forecasting methods have their own advantages and disadvantages.In the future,enterprises can adopt the two sales forecasting methods in parallel to maximize the utilization advantage of sales forecasting for enterprises.

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.

Augmented Winter’s method for forecasting under asynchronous seasonalities

The method of Winters(1960)is one of the most well-known forecasting methodologies in practice.The main reason behind its popularity is that it is easy to implement and can give quite effective and efficient results for practice purposes.However,this method is not capable of capturing a pattern being emerged due to the simultaneous effects of two different asynchronous calendars,such as Gregorian and Hijri.We adapt this method in a way that it can deal with such patterns,and study its performance using a real dataset collected from a brewery factory in Turkey.With the same data set,we also provide a comparative performance analysis between our model and several forecasting models such as Winter’s(Winters 1960),TBAT(De Livera et al.2011),ETS(Hyndman et al.2002),and ARIMA(Hyndman and Khandakar 2008).The results we obtained reveal that better forecasts can be achieved using the new method when two asynchronous calendars exert their effects on the time-series.