Abstract: Interval forecasts have significant advantages in providing uncertainty estimation to point forecasts, leading to the importance of providing prediction intervals (PIs) as well as point forecasts. In this paper, we propose a general feature-based time series forecasting framework, which is divided into “offline” and “online” parts. In the “offline” part, we explore how time series features connect with the prediction interval forecasting accuracy of different forecasting methods by exploring generalized additive models (GAMs), which makes our proposed framework interpretable in the effects of features on the interval forecasting accuracy. Our proposed framework is in essence a model averaging process and we introduce a threshold ratio for the selection of individual forecasting methods in this process. In the “online” part, we calculate the point forecasts and PIs of new series by pre-trained GAMs and the corresponding optimal threshold ratio. We illustrate that our feature-based forecasting framework outperforms all individual benchmark forecasting methods on M3 competition data, with an improved computational efficiency.