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.
ETTh1 and ETTh2 are sampled at an hourly frequency, while ETTm1 and ETTm2 are collected at 15-minute intervals
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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.