The field of time series forecasting is moving towards the development of more efficient and accurate models that can handle complex data and multiple forecasting horizons. Researchers are exploring new architectures and techniques, such as neural architecture search, quadratic direct forecasting, and temporal fusion transformers, to improve the performance of forecasting models. These innovative approaches are showing promising results in various applications, including energy production, retail sales, and industrial machinery maintenance. Notably, the use of hybrid models that combine different techniques, such as transformers and recurrent neural networks, is becoming increasingly popular. Additionally, there is a growing interest in developing models that can handle multivariate time series data and provide probabilistic forecasts. Overall, the field is advancing rapidly, with a focus on developing more robust, efficient, and interpretable models that can be applied in a wide range of domains. Noteworthy papers include: Neural Architecture Search for global multi-step Forecasting of Energy Production Time Series, which introduces a novel framework for automated discovery of time series models. Temporal Fusion Transformer for Multi-Horizon Probabilistic Forecasting of Weekly Retail Sales, which presents a novel study on weekly retail sales forecasting using a temporal fusion transformer. Hydra: Dual Exponentiated Memory for Multivariate Time Series Analysis, which proposes a new meta in-context memory module that learns to memorize patterns at test time. ForecastGAN: A Decomposition-Based Adversarial Framework for Multi-Horizon Time Series Forecasting, which introduces a novel decomposition-based adversarial framework for multi-horizon time series forecasting.