Wavelet-Enhanced Hybrid LSTM-XGBoost Model for Predicting Time Series Containing Unpredictable Events
Accurate electricity consumption forecasting is essential for effective power management, especially in the presence of unpredictable events that disrupt typical consumption patterns. Using the COVID-19 pandemic as a case study for such unpredictable events, this study proposes an improved hybrid LS...
Saved in:
| Main Authors: | Ali Ajder, Hisham A. A. Hamza, Ramazan Ayaz |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10946099/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Waveel: a wavelet-based ensemble learning framework for time series forecasting
by: Xiaohou Shi, et al.
Published: (2025-07-01) -
Simulation Study to Identify Factors Affecting the Performance of LSTM and XGBoost for Anomaly Detection on Labeled Time Series Data
by: Muhammad Rizky Nurhambali, et al.
Published: (2025-08-01) -
Deep Learning vs. Gradient Boosting: Optimizing Transport Energy Forecasts in Thailand Through LSTM and XGBoost
by: Thanapong Champahom, et al.
Published: (2025-03-01) -
Image Enhancement Based on Wavelet Transform and Contrast Limited Adaptive Histogram Equalization
by: YU Tian-he, et al.
Published: (2018-12-01) -
Multi-focus Image Fusion Based on Non-subsampled Discrete Wavelet Transform
by: GUO Ling-xin, et al.
Published: (2018-02-01)