There has been a growing interest in the field of neural networks for prediction in recent
years. In this research, a public dataset including the sales history of a retail store is …

Existing sales forecasting models are not comprehensive and flexible enough to consider
dynamic changes and nonlinearities in sales time-series at the store and product levels. To …

Time series forecasting using historical data is significantly important nowadays. Many fields
such as finance, industries, healthcare, and meteorology use it. Profit analysis using …

To encourage proper employee scheduling for managing crew load, restaurants need
accurate sales forecasting. This paper proposes a case study on many machine learning …

With the penetration of deep learning technology into forecasting and decision support
systems, enterprises have an increasingly urgent need for accurate forecasting of time …

In this paper, we propose seasonal long short-term memory (SLSTM), which is a method for
predicting the sales of agricultural products, to stabilize supply and demand. The SLSTM …

In recent years, the rapid development of e-commerce has brought great convenience to
people. Compared with traditional business environment, e-commerce is more dynamic and …

This paper proposes a novel forecasting method that combines the deep learning method–
long short-term memory (LSTM) networks and random forest (RF). The proposed method …

Forecasting time series data is an important subject in economics, business, and finance.
Traditionally, there are several techniques to effectively forecast the next lag of time series …

Forecasting techniques are used in the various problem domains such as-sales, banking,
healthcare, stock market, etc. The time-series dataset has time-related information that is …