Abstract
All companies need an effective method to predict future sales, and several classic statistical methods exist and are heavily used in the industry. This work proposes a novel sales prediction method based on Convolutional Neural Networks. This type of neural network is generally used for image processing tasks. But in this work, we explore new applications and develop models that produce good results in sales prediction for real pharmaceutical product data. Also, we implemented several classical and statistical prediction methods, and we compared them with our proposed model. For this, we used three comparison metrics: prediction accuracy, number of weights, and number of iterations. Finally, we proceeded to determine which prediction method is better both in accuracy and efficiency terms.
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Velastegui, R., Zhinin-Vera, L., Pilliza, G.E., Chang, O. (2021). Time Series Prediction by Using Convolutional Neural Networks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1. FTC 2020. Advances in Intelligent Systems and Computing, vol 1288. Springer, Cham. https://doi.org/10.1007/978-3-030-63128-4_38
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DOI: https://doi.org/10.1007/978-3-030-63128-4_38
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