AI RESEARCH
Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting
arXiv CS.LG
•
ArXi:2605.13678v1 Announce Type: new Recent studies on long-term time series forecasting have shown that simple linear models and MLP-based predictors can achieve strong performance without increasingly complex architectures. However, many competitive baselines still rely on structural priors such as frequency-domain modeling, explicit decomposition, multi-scale mixing, or sophisticated cross-variable interaction modules, while paying less attention to how simple temporal mappings should be trained and organized.