STEP: Long-Term Time Series Forecasting Through the Lens of the Forecast Start

Long-term time-series forecasting typically focuses on modeling the distant future, yet prediction becomes increasingly uncertain as the forecast horizon grows. We revisit this problem from the forecast start and argue that it may play a larger role in long-horizon accuracy than its position suggests: improving the forecast start can benefit subsequent forecasting. We propose STEP, which improves forecasting by providing a more accurate initial forecast and further strengthening the forecast start, while also serving as an enhancement for other forecasting backbones. First, STEP encodes adjacent temporal evolution through historical differences, then enriches these representations with learned feature shifts, cross-variable interaction, and subspace interaction to improve the initial forecast. Second, STEP treats the initial forecast as a natural continuation of history and jointly models the two, reinforced by a transition-aware training objective that explicitly emphasizes the forecast start. Experiments on multiple real-world datasets show that STEP achieves competitive forecasting performance and improves diverse forecasting backbones. With training-based integration, STEP improves seven different backbones in 96.4% of the evaluated settings. At inference time, STEP can provide an improved forecast start to a frozen backbone, reducing subsequent forecasting error. Code is available at https://anonymous.4open.science/r/STEP-DDD5.