Range Time Series Forecasting

Range time series forecasting aims to accurately predict future values in time series data over extended periods, addressing the challenges posed by long-term dependencies and complex patterns. Current research focuses on developing novel architectures, such as hybrid models combining the strengths of recurrent neural networks (RNNs), transformers, and state-space models (SSMs) like Mamba, to improve both accuracy and efficiency. These advancements are crucial for various applications, including weather prediction, financial modeling, and resource management, where accurate long-range forecasts are essential for informed decision-making. Furthermore, research explores federated learning approaches to address data privacy and scalability concerns in large-scale forecasting tasks.