Recent discussions highlight advancements in time series forecasting models, particularly focusing on the xLSTM-TS model. A study from Dublin City University evaluates various deep learning models, including xLSTM-TS, TCN, N-BEATS, TFT, N-HiTS, and TiDE, for stock market trend prediction. The xLSTM-TS model has been noted for its superior performance in predicting stock price movements compared to other models. Additionally, critiques have emerged regarding the xLSTM's purported 73% accuracy in stock market predictions, suggesting that such claims may lack substance. Experts recommend leveraging traditional models like ARIMA and GARCH to enhance forecasting accuracy, emphasizing the importance of comprehensive benchmarking in evaluating these advanced methodologies.