Abstract
Forecasting product sales in a consumer product company is one of the most important and challenging tasks for business financial planning and inventory management. In this paper, we are proposing a novel forecasting scheme through unique data pre-processing pipeline and machine learning model using a combination of Ensemble Empirical Mode Decomposition (EEMD) and Extreme Learning Machine (ELM) for multivariate sales data comprising of macroeconomic indicators. EEMD decomposition is performed on sales time series and intrinsic mode functions are analyzed. EEMD is also employed to find important constituent parts of indicators as a data pre-processing step. Experimental results show that EEMD helps in improving forecasting performance. We are also using auto-regressive sale terms along with macroeconomic indicators and employing feature ranking to analyze the most impactful features for prediction performance. Final forecasting performance is further compared with state of art techniques used for multivariate sales prediction models namely Vector Auto-Regressive (VAR) and Least Absolute Shrinkage and Selection Operator (LASSO) models. The proposed forecasting scheme demonstrates better performance than the current state of the art techniques.
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Acknowledgements
Many thanks to Dr. Manish Sharma for his numerous valuable suggestions during this research and Mrs. Harshita Bhatia for her support in dataset collection. We would also like to express our gratitude to all the senior management of Samsung R&D Institute, Delhi especially Mr. Pankaj Mishra for supporting our work.
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Sharma, A.K., Bathula, S., Saha, K. (2022). Method for Improvement of Product Sales Forecast for Long Horizon Using Hybrid Decomposition and Machine Learning on Multi-variate Time Series Data. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_27
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DOI: https://doi.org/10.1007/978-981-16-3690-5_27
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