AverageLinear: Enhance Long-Term Time series forcasting with simple averaging

Long-term time series prediction involves forecasting future trends over extended periods based on historical changes. This approach is crucial in various fields such as weather [1], traffic [2], and power [3]. The exceptionally long forecast horizon and the complex correlations between channels pose significant challenges to modeling. Traditional methods often fall short in capturing the sequence and inter-channel relationships. In contrast, deep learning architectures, with their superior fitting capabilities, have emerged as effective tools for addressing long-term time series prediction. Consequently, the primary methodologies in this field have shifted towards deep learning models. The core issue in long time series analysis is extracting dependencies within sequences and correlations across channels, which significantly benefits model performance in multi-channel prediction and robustness. Various methods have been developed to capture this information from time series data. Commonly used techniques include Transformers [4, 5, 6, 7, 8, 9, 10], which apply attention mechanisms to effectively capture correlations both within sequences and across channels; Convolutional Neural Networks (CNN) [11, 12] that use 1D or multidimensional convolutions to capture these dependencies; and structures based on Multilayer Perceptrons[13, 14, 15, 16, 17], such as DLinear, which decompose sequences and apply multiple linear layers to capture sequence correlations.