Abstract
Trend analysis, a database method for condition identification, is widely used in engineering. The sliding window method is an important method for trend analysis. However, the original sliding window method uses an invariant preset threshold and a fixed initial window, which will lead to inaccurate segmentation and long processing time. To solve this problem, it is a reasonable choice to improve the original scheme with dynamic threshold and dynamic initial window. In this paper, a trend extraction method based on improved sliding window is proposed, which can extract the trend characteristics of variables accurately and quickly.
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This research was funded by the National Natural Science Foundation of China (grant number 61672226).
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Lu, M., Sun, Y., Duan, H., Chen, Z. (2021). A Trend Extraction Method Based on Improved Sliding Window. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_40
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DOI: https://doi.org/10.1007/978-981-15-3753-0_40
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