KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction

Time series forecasting is a critical task in domains such as energy, finance, and meteorology, where accurate long-term predictions are essential. While Transformer-based models have shown promise in capturing temporal dependencies, their application to extended sequences is limited by computational inefficiencies and limited generalization. In this study, we propose KEDformer, a knowledge extraction-driven framework that integrates seasonal-trend decomposition to address these challenges. KEDformer leverages knowledge extraction methods that focus on the most informative weights within the self-attention mechanism to reduce computational overhead. Additionally, the proposed KEDformer framework decouples time series into seasonal and trend components. This decomposition enhances the model's ability to capture both short-term fluctuations and long-term patterns. Extensive experiments on five public datasets from energy, transportation, and weather domains demonstrate the effectiveness and competitiveness of KEDformer, providing an efficient solution for long-term time series forecasting.

An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework

The pervasive use of the Internet and social media introduces significant challenges to automated sentiment analysis, particularly for sarcastic expressions in user-generated content. Sarcasm conveys negative emotions through ostensibly positive or exaggerated language, complicating its detection within natural language processing tasks. To address this, we propose an innovative sarcasm detection model integrating Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Multi-Head Attention mechanisms. The CNN component captures local n-gram features, while GRU and LSTM layers model sequential dependencies and contextual information. Multi-Head Attention enhances the model's focus on relevant parts of the input, improving interpretability. Experiments on two sarcasm detection datasets, Headlines and Riloff, demonstrate that the model achieves an accuracy of 81.20% and an F1 score of 80.77% on Headlines, and an accuracy of 79.72% with an F1 score of 61.39% on Riloff, outperforming traditional models. These results validate the effectiveness of our hybrid approach for sarcasm detection in social media texts.