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2 February 2023 Spare parts demand forecasting based on ARMA model
Xi Ren, Xiao-fei Zhang
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Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 124620G (2023) https://doi.org/10.1117/12.2661048
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China
Abstract
Starting from the time series of factors, the level of analysis time, data types, and forecasting accuracy, based on the characteristics of the data sequence to be analyzed. ARMA model to predict sequence requirements must be stable, that factors in the time range of the study subjects must be subjected to the same requirements. If the given sequence is not stationary sequence, you must do on a given sequence of preprocess, smoothing it, then by ARMA model. Example is analyzed by Eview software, the validity of the model is verified.
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Xi Ren and Xiao-fei Zhang "Spare parts demand forecasting based on ARMA model", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 124620G (2 February 2023); https://doi.org/10.1117/12.2661048
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KEYWORDS
Data modeling
Autoregressive models
Statistical analysis
Statistical modeling
Analytical research
Error analysis
Time series analysis
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