Transformer-based and MLP-based methods have emerged as leading approaches in time series forecasting (TSF). While Transformer-based methods excel in capturing long-range dependencies, they suffer from high computational complexities and tend to overfit. Conversely, MLP-based methods offer computational efficiency and adeptness in modeling temporal dynamics, but they struggle with capturing complex temporal patterns effectively. To address these challenges, we propose a novel MLP-based Adaptive Multi-Scale Decomposition (AMD) framework for TSF. Our framework decomposes time series into distinct temporal patterns at multiple scales, leveraging the Multi-Scale Decomposable Mixing (MDM) block to dissect and aggregate these patterns in a residual manner. Complemented by the Dual Dependency Interaction (DDI) block and the Adaptive Multi-predictor Synthesis (AMS) block, our approach effectively models both temporal and channel dependencies and utilizes autocorrelation to refine multi-scale data integration. Comprehensive experiments demonstrate that our AMD framework not only overcomes the limitations of existing methods but also consistently achieves state-of-the-art performance in both long-term and short-term forecasting tasks across various datasets, showcasing superior efficiency. Code is available at https://github.com/TROUBADOUR000/AMD
KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting
Shanghai Advanced Research Institute, Chinese Academy of Sciences（上海先进研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Jiaotong University（北京交通大学）
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
University of Electronic Science and Technology of China（电子科技大学）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Cambridge（剑桥大学）
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
Hong Kong Generative AI Research and Development Center（香港生成式人工智能研发中心）；University of Technology Sydney（悉尼科技大学）；University of New South Wales（新南威尔士大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；A*STAR（新加坡科技研究局）；AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）AI 学科）；CSE, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）
ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
School of Data Science and Engineering, East China Normal University, Shanghai, China（东华大学数据科学与工程学院，上海，中国）；Engineering Research Center of Blockchain Data Management, Ministry of Education, China（教育部区块链数据管理工程研究中心，中国）
AdaMixT: Adaptive Weighted Mixture of Multi-Scale Expert Transformers for Time Series Forecasting
School of Computer Science, Peking University（北京大学计算机科学系）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；Tsinghua University（清华大学）
Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application
School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；School of Internet, Anhui University（安徽大学互联网学院）；Chinese Academy of Sciences, Institute of Plasma Physics（中国科学院等离子体物理研究所）；School of Software Technology, Zhejiang University（浙江大学软件学院）
QGAPHEnsemble : Combining Hybrid QLSTM Network Ensemble via Adaptive Weighting for Short Term Weather Forecasting
Georgia Institute of Technology（佐治亚理工学院）；Indian Institute of Engineering Science and Technology, Shibpur（印度工程科学技术学院（希布普尔））；IIT Roorkee（鲁基印度理工学院）；National Institute of Technology, Rourkela（罗尔克拉国家理工学院）；National Institute of Technology Karnataka（卡纳塔克邦国家理工学院）；Jadavpur University（贾达沃普尔大学）
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；Southern University of Science and Technology（南方科技大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Tokyo（东京大学）；University of New South Wales（新南威尔士大学）；Tencent WXG（腾讯微信事业群）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）；School of Management, Xi'an Jiaotong University（西安交通大学管理学院）
Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting
University of North Dakota（北达科他大学）；Czech Technical University in Prague（捷克布拉格技术大学）；International University of Rabat（拉巴特国际大学）；University of Leeds（利兹大学）；Mohammed V University in Rabat（拉巴特穆罕默德五世大学）
A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting
University of Science and Technology of China（科学技术大学）；Shanghai Black Wing Asset Management Co., Ltd.（上海黑翼资产管理有限公司）；Di-Matrix(Shanghai) Information Technology Co., Ltd.（迪矩阵（上海）信息技术有限公司）；The Chinese University of Hong Kong（香港中文大学）
End-to-End Neural Decomposition with Koopman Operators for Time-Series Forecasting
National Taiwan University（台湾大学）；National Institute of Information and Communications Technology（信息通信研究机构）；Research Center for Information Technology Innovation（资讯科技创新研究中心）
