Open Access
| Issue |
E3S Web Conf.
Volume 256, 2021
2021 International Conference on Power System and Energy Internet (PoSEI2021)
|
|
|---|---|---|
| Article Number | 02038 | |
| Number of page(s) | 4 | |
| Section | Energy Internet R&D and Smart Energy Application | |
| DOI | https://doi.org/10.1051/e3sconf/202125602038 | |
| Published online | 10 May 2021 | |
- Btthpa, Cmpca, Cal, et al. (2020) Dynamic time warping-based imputation for univariate time series data -ScienceDirect[J]. Pattern Recognition Letters, 139(10):139–147. [Google Scholar]
- Gundersen K, Alendal G, Oleynik A, et al. (2020) Binary Time Series Classification with Bayesian Convolutional Neural Networks When Monitoring for Marine Gas Discharges[J]. Algorithms, 13(6):145. [Google Scholar]
- Ambrosino F, L. Thinová, Briestensk M, et al. (2020) Detecting time series anomalies using hybrid methods applied to Radon signals recorded in caves for possible correlation with earthquakes[J]. Acta Geodaetica et Geophysica, 55(3):405–420. [Google Scholar]
- Melin P, Daniela Sánchez, Monica J C, et al. (2021) Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction[J]. Soft Computing, (10):1–38. [Google Scholar]
- Churchill R M, Tobias B, Zhu Y. (2020) Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data[J]. Physics of Plasmas, 27(6):062510. [Google Scholar]
- Zhang D, Chen S, Ling L, et al. (2020) Forecasting agricultural commodity prices using model selection framework with time series features and forecast horizons[J]. IEEE Access, PP(99):1–1. [Google Scholar]
- Laureniu, Opincariu, Norbert, et al. (2019) Edge computing in space: Field programmable gate array-based solutions for spectral and probabilistic analysis of time series.[J]. The Review of scientific instruments, 90(11):114501–114501. [Google Scholar]
- Churchill R M, Tobias B, Zhu Y. (2020) Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data[J]. Physics of Plasmas, 27(6):062510. [Google Scholar]
- Tan W, Xing J, Yang S, et al. (2020) Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation[J]. Water, 12(8):2178. [Google Scholar]
- Juan José Vidal Macua, José Manuel Nicolau, Vicente E, et al. (2020) Assessing vegetation recovery in reclaimed opencast mines of the Teruel coalfield (Spain) using Landsat time series and boosted regression trees[J]. Science of The Total Environment, 717(10):137250. [Google Scholar]
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