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10.5120/ijca2021921283 |
Ghita Rguiga and Jamal Benhra. Multisteps Sales Forecasting using LSTM Compared to GMDH. International Journal of Computer Applications 183(1):46-50, May 2021. BibTeX
@article{10.5120/ijca2021921283, author = {Ghita Rguiga and Jamal Benhra}, title = {Multisteps Sales Forecasting using LSTM Compared to GMDH}, journal = {International Journal of Computer Applications}, issue_date = {May 2021}, volume = {183}, number = {1}, month = {May}, year = {2021}, issn = {0975-8887}, pages = {46-50}, numpages = {5}, url = {http://www.ijcaonline.org/archives/volume183/number1/31895-2021921283}, doi = {10.5120/ijca2021921283}, publisher = {Foundation of Computer Science (FCS), NY, USA}, address = {New York, USA} }
Abstract
This work reports the effectiveness of using Recurrent Neural Network LSTMs for multistep sales forecasting instead of a standalone approach and the use of time series data, treatment of inflation, forecasting options and tunings to help decision executives make better decisions on forecast and management systems. This work is lead on historical sales based on a year of demand records, the program established using LSTMs showed minimal loss and optimal forecasts compared to prediction using GMDH which is known for management forecasts such as inventories and sales forecasting.
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Keywords
LSTM, RNN, GMDH, Sales Forecasting, Artificial Intelligence, Supply Chain Management