Time Series Forecasting Model

Time series forecasting (TSF) models aim to predict future values based on historical data, a crucial task across diverse fields. Current research emphasizes developing more efficient and generalizable models, exploring architectures like transformers and employing techniques such as wavelet transforms and information bottleneck methods to improve accuracy and reduce hyperparameter tuning. Significant efforts focus on automating model selection and addressing challenges like non-stationarity and the effective extraction of relevant features from complex datasets, ultimately aiming to enhance the reliability and applicability of TSF in real-world scenarios. The resulting improvements in forecasting accuracy have broad implications for applications ranging from financial markets to environmental monitoring.