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Singular spectrum analysis and forecasting of failure time series

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  • Rocco S, Claudio M.

Abstract

Singular spectrum analysis (SSA) is a relatively recent approach used to model time series with no assumptions of the underlying process. SSA is able to make a decomposition of the original time series into the sum of independent components, which represent the trend, oscillatory behavior (periodic or quasi-periodic components) and noise. In this paper SSA is used to decompose and forecast failure behaviors using time series related to time-to-failure data. Results are compared with previous approaches and show that SSA is a promising approach for data analysis and for forecasting failure time series.

Suggested Citation

  • Rocco S, Claudio M., 2013. "Singular spectrum analysis and forecasting of failure time series," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 126-136.
    • Handle: RePEc:eee:reensy:v:114:y:2013:i:c:p:126-136
    DOI: 10.1016/j.ress.2013.01.007
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Feiyu Zhang & Yuqi Dong & Kequan Zhang, 2016. "A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China," Sustainability, MDPI, vol. 8(6), pages 1-20, June.
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    6. Aman Kalteh, 2016. "Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 747-766, January.
    7. Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.

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