TimeBase: The Power of Minimalism in Efficient Long-term Time Series Forecasting

Long-term time series forecasting (LTSF) has traditionally relied on large parameters to capture extended temporal dependencies, resulting in substantial computational costs and inefficiencies in both memory usage and processing time. However, time series data, unlike high-dimensional images or text, often exhibit temporal pattern similarity and low-rank structures, especially in long-term horizons. By leveraging this structure, models can be guided to focus on more essential, concise temporal data, improving both accuracy and computational efficiency. In this paper, we introduce TimeBase, an ultra-lightweight network to harness the power of minimalism in LTSF. TimeBase 1) extracts core basis temporal components and 2) transforms traditional point-level forecasting into efficient segment-level forecasting, achieving optimal utilization of both data and parameters. Extensive experiments on diverse real-world datasets show that TimeBase achieves remarkable efficiency and secures competitive forecasting performance. Additionally, TimeBase can also serve as a very effective plug-and-play complexity reducer for any patch-based forecasting models. Code is available at \url{https://github.com/hqh0728/TimeBase}.

Long-term time series forecasting (LTSF) is important for areas like weather, energy, and finance. But current methods often rely on large, complex models that are slow and costly.We introduce TimeBase, a lightweight forecasting model that makes predictions using only the most essential patterns in the data. Instead of forecasting each time point separately, it predicts meaningful segments, reducing computation while improving accuracy.TimeBase works well across many real-world datasets and can also simplify existing models by acting as a plug-in. This makes accurate long-term forecasting faster and more efficient, even in resource-limited settings.