Time-series forecasting of seasonal items sales using machine learning – A comparative analysis
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 investigated to forecast the sales of furniture. To this aim, several forecasting models are applied. First, some clas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-04-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096822000027 |
| _version_ | 1811244880171630592 |
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| author | Yasaman Ensafi Saman Hassanzadeh Amin Guoqing Zhang Bharat Shah |
| author_facet | Yasaman Ensafi Saman Hassanzadeh Amin Guoqing Zhang Bharat Shah |
| author_sort | Yasaman Ensafi |
| collection | DOAJ |
| description | 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 investigated to forecast the sales of furniture. To this aim, several forecasting models are applied. First, some classical time-series forecasting techniques such as Seasonal Autoregressive Integrated Moving Average (SARIMA) and Triple Exponential Smoothing are utilized. Then, more advanced methods such as Prophet, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) are applied. The performances of the models are compared using different accuracy measurement methods (e.g., Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE)). The results show the superiority of the Stacked LSTM method over the other methods. In addition, the results indicate the good performances of the Prophet and CNN models. |
| first_indexed | 2024-04-12T14:32:31Z |
| format | Article |
| id | doaj.art-71d7eb0047414197aba01673a1eb834e |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-12T14:32:31Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-71d7eb0047414197aba01673a1eb834e2022-12-22T03:29:13ZengElsevierInternational Journal of Information Management Data Insights2667-09682022-04-0121100058Time-series forecasting of seasonal items sales using machine learning – A comparative analysisYasaman Ensafi0Saman Hassanzadeh Amin1Guoqing Zhang2Bharat Shah3Department of Mechanical and Industrial Engineering, Ryerson University, ON, CanadaDepartment of Mechanical and Industrial Engineering, Ryerson University, ON, Canada; Corresponding author.Department of Mechanical, Automotive and Materials Engineering, University of Windsor, ON, CanadaTed Rogers School of Management, Ryerson University, ON, CanadaThere 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 investigated to forecast the sales of furniture. To this aim, several forecasting models are applied. First, some classical time-series forecasting techniques such as Seasonal Autoregressive Integrated Moving Average (SARIMA) and Triple Exponential Smoothing are utilized. Then, more advanced methods such as Prophet, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) are applied. The performances of the models are compared using different accuracy measurement methods (e.g., Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE)). The results show the superiority of the Stacked LSTM method over the other methods. In addition, the results indicate the good performances of the Prophet and CNN models.http://www.sciencedirect.com/science/article/pii/S2667096822000027Time-series forecastingSales forecastingSeasonal itemsNeural networkBig data |
| spellingShingle | Yasaman Ensafi Saman Hassanzadeh Amin Guoqing Zhang Bharat Shah Time-series forecasting of seasonal items sales using machine learning – A comparative analysis International Journal of Information Management Data Insights Time-series forecasting Sales forecasting Seasonal items Neural network Big data |
| title | Time-series forecasting of seasonal items sales using machine learning – A comparative analysis |
| title_full | Time-series forecasting of seasonal items sales using machine learning – A comparative analysis |
| title_fullStr | Time-series forecasting of seasonal items sales using machine learning – A comparative analysis |
| title_full_unstemmed | Time-series forecasting of seasonal items sales using machine learning – A comparative analysis |
| title_short | Time-series forecasting of seasonal items sales using machine learning – A comparative analysis |
| title_sort | time series forecasting of seasonal items sales using machine learning a comparative analysis |
| topic | Time-series forecasting Sales forecasting Seasonal items Neural network Big data |
| url | http://www.sciencedirect.com/science/article/pii/S2667096822000027 |
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