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Time Series Pbkp_rediction by Using Convolutional Neural Networks

Abstract:

All companies need an effective method to pbkp_redict future sales, and several classic statistical methods exist and are heavily used in the industry. This work proposes a novel sales pbkp_rediction 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 pbkp_rediction for real pharmaceutical product data. Also, we implemented several classical and statistical pbkp_rediction methods, and we compared them with our proposed model. For this, we used three comparison metrics: pbkp_rediction accuracy, number of weights, and number of iterations. Finally, we proceeded to determine which pbkp_rediction method is better both in accuracy and efficiency terms.