Deep Time Series Forecasting

Deep time series forecasting aims to accurately predict future values of time-dependent data, a crucial task across numerous domains. Current research heavily focuses on addressing the challenges posed by non-stationary data, employing advanced architectures like Transformers and incorporating techniques such as adaptive normalization and wavelet analysis to improve model robustness and accuracy. These advancements are significant because reliable forecasting is essential for informed decision-making in diverse fields, ranging from resource management and financial markets to environmental monitoring and public health.