Long Term Time Series Forecasting

Long-term time series forecasting aims to accurately predict future values of time series data over extended horizons, a challenging task due to complex temporal dependencies and potential distribution shifts. Current research focuses on developing efficient and accurate models, exploring architectures like Transformers, state-space models (SSMs) such as Mamba, and variations of recurrent neural networks (RNNs) like LSTMs, often incorporating techniques like patching, frequency decomposition, and multi-scale normalization to improve performance. These advancements have significant implications for various fields, including finance, energy, and environmental science, enabling better resource allocation, risk management, and predictive maintenance.