CombinationTS decomposes time-series models into modules and finds that good embeddings let simple identity encoders match complex ones, while input structural priors give better performance-stability trade-offs than complex encoders.
TimeFormer: Trans- former with attention modulation empowered by tempo- ral characteristics for time series forecasting.Expert Systems with Applications, 307:131040, 2026c
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CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models
CombinationTS decomposes time-series models into modules and finds that good embeddings let simple identity encoders match complex ones, while input structural priors give better performance-stability trade-offs than complex encoders.