Total retail sales forecasting method based on CEEMDAN, GPR combined model and CBR correction

Cuiqing Jiang; Lijun Wang

doi:10.1117/12.2627472

22 April 2022 Total retail sales forecasting method based on CEEMDAN, GPR combined model and CBR correction

Cuiqing Jiang, Lijun Wang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 1216324 (2022) https://doi.org/10.1117/12.2627472
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

The total retail sales of social consumer goods is an important index reflecting the consumption level. Predicting its development trend is helpful to grasp the economic development situation. Because the factors affecting the total retail sales of social consumer goods are very complex, it is very difficult to make full use of the value information contained in a single forecasting model. Therefore, this paper proposes a new forecasting method of total retail sales of social consumer goods corrected by combined model and case-based reasoning (CBR). First, a new dataset is constructed by using adaptive noise complete set empirical mode decomposition (CEEMDAN) to eliminate high frequency noise. The differential integration autoregressive moving average (ARIMA), long and short term memory (LSTM), limit gradient enhancement (XGBoost) and support vector regression (SVR) models were established for the new dataset, and then the prediction results of each prediction model were integrated with Gaussian process regression (GPR) to obtain the initial prediction value and error sequence. In addition, in order to solve the problem that the implicit knowledge in the error sequence is difficult to be regularized and quantified by mathematical model, this paper proposes a new error correction method, namely CBR, to improve the prediction accuracy. Experimental results show that compared with single model, the method proposed in this paper has better prediction effect and can effectively improve the prediction accuracy of total retail sales of social consumer goods.

Citation Download Citation

Cuiqing Jiang and Lijun Wang "Total retail sales forecasting method based on CEEMDAN, GPR combined model and CBR correction", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 1216324 (22 April 2022); https://doi.org/10.1117/12.2627472

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KEYWORDS

Data modeling

Autoregressive models

General packet radio service

Mathematical modeling

Denoising

Data processing

Integration

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