Abstract
Granular information has emerged as a potent tool for data representation and processing across various domains. However, existing time series data granulation techniques often overlook the influence of external factors. In this study, a multisource time series data granularity conversion model is proposed that achieves granularity conversion effectively while maintaining result consistency and stability. The model incorporates the impact of external source data using a multivariate linear regression model, and the entropy weighting method is employed to allocate weights and finalize the granularity conversion. Through experimental analysis using Beijing's 2022 air quality dataset, our proposed method outperforms traditional information granulation approaches, providing valuable decision-making insights for industrial system optimization and research.
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Acknowledgements
This work was supported by the National Key R&D Program of China under Grant No. 2020YFB1710200.
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Leng, C., Han, Q., Lu, D. (2023). The Multisource Time Series Data Granularity Conversion Method. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1879. Springer, Singapore. https://doi.org/10.1007/978-981-99-5968-6_13
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DOI: https://doi.org/10.1007/978-981-99-5968-6_13
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