Application of BP neural network to sale forecast for H company

Li, Zheng; Li, Renwang; Shang, Zhaohui; Wang, Haiyan; Chen, Xiu-Lan; Can-lin, MO

doi:10.1109/cscwd.2012.6221835

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…They state that the BPNN algorithm outperforms other statistical algorithms and that by providing additional information on the popularity index gives better accuracy numbers. Li et al (2012) also illustrate that backpropagation neural networks can yield satisfactory results for vehicle sales forecasting. As traditional BPNN algorithms were providing promising results, studies were conducted on improving BPNN networks by adding different extensions to it.…”

Section: Related Workmentioning

confidence: 86%

Sales forecasting using multivariate long short term memory network models

Helmini

Jihan

Jayasinghe

et al. 2019

Preprint

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In the retail domain, estimating the sales before actual sales become known plays a key role in maintaining a successful business. This is due to the fact that most crucial decisions are bound to be based on these forecasts. Statistical sales forecasting models like ARIMA (Auto-Regressive Integrated Moving Average), can be identified as one of the most traditional and commonly used forecasting methodologies. Even though these models are capable of producing satisfactory forecasts for linear time series data they are not suitable for analyzing non-linear data. Therefore, machine learning models (such as Random Forest Regression, XGBoost) have been employed frequently as they were able to achieve better results using non-linear data. The recent research shows that deep learning models (e.g. recurrent neural networks) can provide higher accuracy in predictions compared to machine learning models due to their ability to persist information and identify temporal relationships. In this paper, we adopt a special variant of Long Short Term Memory (LSTM) network called LSTM model with peephole connections for sales prediction. We first build our model using historical features for sales forecasting. We compare the results of this initial LSTM model with multiple machine learning models, namely, the Extreme Gradient Boosting model (XGB) and Random Forest Regressor model(RFR). We further improve the prediction accuracy of the initial model by incorporating features that describe the future that is known to us in the current moment, an approach that has not been explored in previous state-of-the-art LSTM based forecasting models. The initial LSTM model we develop outperforms the machine learning models achieving 12% - 14% improvement whereas the improved LSTM model achieves 11\% - 13\% improvement compared to the improved machine learning models. Furthermore, we also show that our improved LSTM model can obtain a 20% - 21% improvement compared to the initial LSTM model, achieving significant improvement.

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Section: Related Workmentioning

confidence: 86%

Sales forecasting using multivariate long short term memory network models

Helmini

Jihan

Jayasinghe

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Besides, the LEAST SQUARE SUPPORT VECTOR MACHINE model was used to predict the sales of network products, and good similarity calculation was achieved [4]. In the field of artificial neural network, BP artificial neural network is used to forecast the sales data of the company, and highly consistent prediction results are obtained [5].…”

Section: Introductionmentioning

confidence: 99%

Walmart Sales Prediction Based on Decision Tree, Random Forest, and K Neighbors Regressor

Yao¹

2023

HBEM

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Sales forecasting is a very important research direction in the business and academic fields, and sales forecasting methods are also in full bloom, such as time series model, machine learning model and deep neural network model. This paper will use three machine learning models: Decision Tree Regressor, Random Forest Regressor, and K Neighbors Regressor to predict Walmart Recruiting - Store Sales data. Using correlation, mean absolute error, and mean square error to evaluate the prediction results of these three models, it is found that the prediction effect of Random Forest Registrar performs the best of these three models. The R2 value between the predicted sales volume of Random Forest Regressor and the sales volume of the test set is 0.937, the average absolute error is 1937.810, and the mean square error is 32993323.634. Therefore, Walmart can use Random Forest Regressor when forecasting the weekly sales of its own stores. At the same time, this paper provides a good model reference value (especially Random Forest Regressor) for other industries when researching the sales forecast, as well as methods for evaluating different model predictions. Overall, these results shed light on guiding further exploration of Sales forecasts for supermarkets.

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