A Novel Approach to Dairy Sales Forecasting: Multi-Perspective Fusion Bi-LSTM Coupled with Universal Scale CNN
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Abstract
In today's world, accurately predicting sales is important for minimising costs and improving overall profits. A wide range of understanding of organisational performance and future sales can be accomplished through improved customer service strategies. It aids in enhancing product returns and lowering lost sales, leading to more efficient production planning. The sales prediction in dairy products reflects distinctive challenges, predominantly due to the quality of these products, which is closely connected to consumers' health. To overcome the problem, the proposed research em- ploys an effective DL (Deep Learning) based technique for forecasting the sales of dairy products by analysing the dairy goods sales dataset from an openly available website. The proposed research utilises Universal Scale CNN (Convolutional Neural Network), a 1D CNN, which is capable of learning the features at optimal and effective rates. The following features are passed to the Multi-Perspective based Bi-LSTM (Bidirectional Long Short-Term Memory), which is capable of learning features effectively by reducing error rates in predicting sales rates of dairy-based products. The overall performance of the proposed Multi-Perspective Fusion Bi-LSTM with Universal Scale CNN is evaluated with performance metrics, including RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error). These performance metrics evaluate the proposed model's effectiveness in forecasting dairy product sales.
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