Retail Demand Forecasting using CNN-LSTM Model

Nithin, Soundar S J; T, Rajasekar; Jayanthy, S.; Karthik, K; Rithick, Roshan R

doi:10.1109/icears53579.2022.9752283

Cited by 9 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model is trained with the existing products and used for predicting the sales of the new products. [43] Designed a deep neural network model for forecasting the sales of Walmart data. An effective visualization mechanism is also used for enhancing the model.…”

Section: Comparative Study Of Deep Learning Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Study of Data Analytic Techniques for Sales Forecasting

2023

Recent Trends in Data Science and Its Applications

View full text Add to dashboard Cite

Predicting the sales of a product or demand for a service is an utmost need in any industry. With the changes and increase in the channels and modes through which the products are sold, the prediction of sales in the future becomes tedious. The number of features that would help in predicting sales differs and increases. This leads to the usage of Machine Learning and Deep Learning Models in sales forecasting. A deep survey of various works that employ machine learning and Deep learning approaches for sales prediction is done and inferences are made. Every work studied has a list of models which are used as baseline models for comparison and the same is listed for better inferences. Works that address the problem of predicting the sales of new products are also discussed. A list of publicly available sales forecasting datasets is also given.

show abstract

Section: Comparative Study Of Deep Learning Modelsmentioning

confidence: 99%

“…The deep neural network performs well than the linear regression model and support vector machine. [44] Combines the prophet model with the gated recurrent unit for predicting the Sales of the clothes. The combined model performs well than the other individual deep-learning approaches and Time series Models.…”

Section: Comparative Study Of Deep Learning Modelsmentioning

confidence: 99%