Time-series forecasting of seasonal items sales using machine learning – A comparative analysis
International Journal of Information Management Data Insights, cilt.2, sa.1, 2022 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 2 Sayı: 1
- Basım Tarihi: 2022
- Doi Numarası: 10.1016/j.jjimei.2022.100058
- Dergi Adı: International Journal of Information Management Data Insights
- Derginin Tarandığı İndeksler: Scopus
- Anahtar Kelimeler: Big data, Neural network, Sales forecasting, Seasonal items, Time-series forecasting
- TED Üniversitesi Adresli: Hayır
Özet
© 2022 The Author(s)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.