A Support Vector Based Hybrid Forecasting Model for Chaotic Time Series: Spare Part Consumption Prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software
Link
https://link.springer.com/content/pdf/10.1007/s11063-022-10986-4.pdf
Reference36 articles.
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2. Hua Z, Zhang B (2006) A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Appl Math Comput 181(2):1035–1048. https://doi.org/10.1016/j.amc.2006.01.064
3. Guo F, Diao J, Zhao Q, Wang D, Sun Q (2017) A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data. Comput Ind Eng. https://doi.org/10.1016/j.cie.2017.05.002
4. McGovern A, Rosendahl D, Brown R (2011) Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction. Data Min Knowl Disc 22(1–2):232–258. https://doi.org/10.1007/s10618-010-0193-7
5. Bozic M, Stojanovic M, Stajic Z (2013) Mutual information-based inputs selection for electric load time series forecasting. Entropy 15(3):926–942. https://doi.org/10.3390/e15030926
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