Design and Develop Data Analysis and Forecasting of the Sales Using Machine Learning

Kadam, Vinod J.; Vhatkar, Sangeeta

doi:10.1007/978-981-16-4863-2_14

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The RNN structure is shown in Figure 2, where x is the input at the current time, h is the output at the current time, and A is the network neuron. Compared to RNN, the variant structure of LSTM makes it more suitable for processing long series data and has stronger modeling capabilities [7]. LSTM is an advanced recurrent neural network, which uses three independent logic control units, namely, forgetting gate, output gate and input gate, to realize the deletion of unimportant information and the control of the input and output of new information, thus greatly improving the reliability and memory function of the system, and effectively solving the problem of gradient disappearance in traditional RNN.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Research on Product Demand Forecasting and Personalized Recommendation Based on Data Analysis

Yuan

2024

IJCSIT

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This paper aims to explore the design and implementation of product demand forecasting and personalized recommendation system based on data analysis. Through the collection and analysis of a large number of user behavior data, an accurate demand prediction model is constructed, and based on this, a personalized recommendation algorithm is implemented. The paper first introduces the research background and purpose, and then elaborates the research method, process and results. The method based on data analysis can effectively predict the product demand and provide users with personalized recommendation services, so as to improve user satisfaction and market competitiveness of enterprises.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Research on Product Demand Forecasting and Personalized Recommendation Based on Data Analysis

Yuan

2024

IJCSIT

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“…In practical applications, a common approach to improving forecasting accuracy is to combine time series analysis and regression analysis, leading to the development of innovative "hybrid" methods. Examples of such hybrids include ARMA [6] and ARIMA [11][12][13]. ARIMA, a fusion of ARMA and differential operations, is recognized as one of the most widely used techniques in time series analysis.…”

Section: Related Workmentioning

confidence: 99%

Achieving Sales Forecasting with Higher Accuracy and Efficiency: A New Model Based on Modified Transformer

Li,

2023

JTAER

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With the exponential expansion of e-commerce, an immense volume of historical sales data has been generated and amassed. This influx of data has created an opportunity for more accurate sales forecasting. While various sales forecasting methods and models have been applied in practice, existing ones often struggle to fully harness sales data and manage significant fluctuations. As a result, they frequently fail to make accurate predictions, falling short of meeting enterprise needs. Therefore, it is imperative to explore new models to enhance the accuracy and efficiency of sales forecasting. In this paper, we introduce a model tailored for sales forecasting based on a Transformer with encoder–decoder architecture and multi-head attention mechanisms. We have made specific modifications to the standard Transformer model, such as removing the Softmax layer in the last layer and adapting input embedding, position encoding, and feedforward network components to align with the unique characteristics of sales forecast data and the specific requirements of sales forecasting. The multi-head attention mechanism in our proposed model can directly compute the dot product results in a single step, addressing long-term time-dependent computation challenges while maintaining lower time complexity and greater interpretability. This enhancement significantly contributes to improving the model’s accuracy and efficiency. Furthermore, we provide a comprehensive formula representation of the model for the first time, facilitating better understanding and implementation. We conducted experiments using sales datasets that incorporate various factors influencing sales forecasts, such as seasons, holidays, and promotions. The results demonstrate that our proposed model significantly outperforms seven selected benchmark methods, reducing RMSLE, RMSWLE, NWRMSLE, and RMALE by approximately 48.2%, 48.5%, 45.2, and 63.0%, respectively. Additionally, ablation experiments on the multi-head attention and the number of encoder–decoders validate the rationality of our chosen model parameters.

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Design and Develop Data Analysis and Forecasting of the Sales Using Machine Learning

Cited by 2 publications

References 16 publications

Research on Product Demand Forecasting and Personalized Recommendation Based on Data Analysis

Research on Product Demand Forecasting and Personalized Recommendation Based on Data Analysis

Achieving Sales Forecasting with Higher Accuracy and Efficiency: A New Model Based on Modified Transformer

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