The field of time series forecasting is experiencing significant growth, with a focus on improving the accuracy and efficiency of forecasting models. Recent developments have centered around incorporating external information, such as exogenous inputs, to enhance model performance. Additionally, there is a growing interest in decomposing the time series forecasting pipeline into modular components, allowing for more effective sequence representation, information extraction, and target projection. Other notable trends include the use of deep learning architectures, such as transformers and convolutional layers, to extract spatial-temporal dependencies and model complex patterns in time series data. Furthermore, researchers are exploring the development of foundation models that can generalize across diverse prediction tasks and scenarios, as well as techniques for interpreting and explaining the behavior of time series models. Noteworthy papers include Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction, which proposes a method for whitening exogenous inputs to reduce redundancy and improve forecasting performance. Another significant contribution is the paper Decomposing the Time Series Forecasting Pipeline, which achieves state-of-the-art forecasting accuracy while enhancing computational efficiency. The paper MoFE-Time also stands out for its innovative approach to integrating time and frequency domain features within a Mixture of Experts network, achieving new state-of-the-art performance on several benchmarks.