A Denoising Time Window Algorithm for Optimizing LSTM Prediction
Realistic anomalous data noise is usually locally distributed in nature. To address the challenge posed by clustered anomalous noise in time series data, which not only reduces the accuracy of forecasting models but also presents a unique challenge due to its clustered nature and existing methods&am...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10537184/ |