Learning Less is More: Adaptive Modular Foundation Model for Time Series Forecasting

Many existing time series forecasting methods assume that input time series exhibit specific properties, such as seasonality, irregularity, or non-stationarity, and design network modules with these assumptions. Within these methods, foundation models often cover network modules with different assumptions, in order to learn input time series with diverse properties. However, such foundation models process each input time series through all modules, regardless of whether their property assumptions match the input, potentially reducing forecasting accuracy. To address this challenge, we propose ReTS, an adaptive modular foundation model for time series forecasting, where only modules with matched property assumptions assemble together for different input time series at run time. First, by decomposing popular time series forecasting models, we reveal the property assumptions of their network modules, and rearrange these models into a unified recurrent activation structure. Based on this unified structure, ReTS is proposed, which adaptively excludes modules with mismatched property assumptions for each input, thereby avoiding imposing properties the input does not exhibit. Furthermore, the activated structures of ReTS can closely resemble the structures of different baselines for different inputs, and even form unprecedented ones. Experiments on eighteen datasets demonstrate competitive performance against nineteen baselines, with 91% less computational cost at inference than the next-smallest pre-trained baseline. Our code is available at https://anonymous.4open.science/r/ReTS-6307/.