Open access
Aug 2026
Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting
A similarity matching (SM)‐based method that leverages historical time series patterns from analogous entities is introduced that outperformed 13 baseline models, including state‐of‐the‐art techniques, across two real‐world time series datasets.
Hanbyeol Park, Sunghyun Sim, K. Park et al.
· Journal of Forecasting · 0 citations