STLNet: Seasonal-Trend-LongTerm Forecasting Network

In the field of resource allocation, time series forecasting is crucial for optimizing resource distribution and improving operational efficiency. The core challenge lies in accurately capturing changes in the input sequence to ensure highly accurate forecasting results. Existing methods typically first capture local features of the input sequence, followed by capturing global features using a self-attention mechanism for forecasting. However, these methods suffer from high computational complexity and insufficient feature extraction. In complex scenarios, these shortcomings significantly impact forecasting results. To address this issue, this paper proposes the STLNet (Seasonal-Trend-LongTerm Forecasting Network) model, aiming to deeply capture both trend and seasonal features of sequences, achieving higher forecasting accuracy while reducing computational complexity. Specifically, STLNet combines the extraction of trend and seasonal features and introduces an interval information forecasting layer with stochastic pooling for trend forecasting, enabling more accurate forecasting of trend data. Additionally, for seasonal data forecasting, the model not only considers the correlations within channels but also captures inter-channel correlations through a channel interaction convolution layer. We tested STLNet on several public datasets, and the results show that the model significantly outperforms state-of-the-art methods in terms of accuracy.