<ul><li>Rapid expansion of model size in time series forecasting poses a challenge, with models growing from early Transformers to recent architectures like TimesNet.</li><li>Alinear is introduced as an ultra-lightweight forecasting model that competes with larger models using minimal parameters, utilizing a horizon-aware adaptive decomposition mechanism and progressive frequency attenuation strategy.</li><li>Extensive experiments on seven benchmark datasets show that Alinear outperforms large-scale models while using less than 1% of their parameters across various forecasting horizons.</li><li>The study challenges the belief that larger models are inherently superior in time series forecasting and advocates for a shift towards more efficient modeling approaches.</li></ul>

Does Scaling Law Apply in Time Series Forecasting?

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