Cargando…
A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example
In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water fill...
Autores principales: | Song, Wei, Gao, Chao, Zhao, Yue, Zhao, Yandong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571071/ https://www.ncbi.nlm.nih.gov/pubmed/32899485 http://dx.doi.org/10.3390/s20185045 |
Ejemplares similares
-
An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model
por: Lv, Jiehua, et al.
Publicado: (2021) -
Prediction of Research Hotspots Based on LSTM: Taking Information Science as Example
por: Xiang, Fuzhong
Publicado: (2022) -
Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau
por: Zhao, Lufei, et al.
Publicado: (2023) -
Using LSTM and PSO techniques for predicting moisture content of poplar fibers by Impulse-cyclone Drying
por: Chen, Feng, et al.
Publicado: (2022) -
LSTM-Based VAE-GAN for Time-Series Anomaly Detection
por: Niu, Zijian, et al.
Publicado: (2020)