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ARIMA model analyzes the tendency and challenges of intelligent marketing in the era of digitalization

Lingji Wang

Lingji Wang

Business College, Geely University of ChinaChengdu, China
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Wang, Lingji
  
24 set 2025
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Pubblicato online: 24 set 2025
Ricevuto: 28 dic 2024
Accettato: 17 apr 2025
DOI: https://doi.org/10.2478/amns-2025-0984
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© 2025 Lingji Wang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

The ARIMA model builds the process
The ARIMA model builds the process

Figure 2.

Raw data time sequence diagram
Raw data time sequence diagram

Figure 3.

The time sequence diagram of the first order difference
The time sequence diagram of the first order difference

Figure 4.

Time sequence diagram of the seasonal difference
Time sequence diagram of the seasonal difference

Figure 5.

The ACF and PACF diagram of the difference sequence
The ACF and PACF diagram of the difference sequence

Figure 6.

Comparison of sales forecast results based on ARIMA model
Comparison of sales forecast results based on ARIMA model

Figure 7.

Portfolio model week sales forecast
Portfolio model week sales forecast

Figure 8.

Three model error comparisons
Three model error comparisons

The optimal weight of the combination prediction model

Set Expectation Forecast Accuracy Weighted moving average Once the index is smooth Secondary index smoothing Three times the index smoothness ARIMA Combination prediction
Test set 6 MAE 256 397 251 204 224 298
Test set 6 MSE 45767 178937 79004 75232 178236 122365
Test set 6 MAPE 32% 52% 37.5% 35.1% 37.7% 21.4%
Weight 12.32% 20.41% 22.07% 0.0% 45.2% 100.0%
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