Time series data—those streams of information collected over time—are everywhere, from stock prices to heartbeats. They hold the key to understanding trends, predicting the future, and spotting anomalies. But analyzing this data can be tricky. Recently, researchers have wondered: could large language models (LLMs), known for their prowess in natural language processing, also unlock the secrets hidden within time series? A new study dives into this question, comparing LLMs to traditional methods across several key tasks: classification (labeling patterns), anomaly detection (finding the unusual), and forecasting (predicting the future). The results? It's a mixed bag. While LLMs showed promise in anomaly detection, their performance in forecasting lagged behind simpler, specialized models. In tasks like classifying medical data or spotting irregularities in server performance, LLMs outshone their non-LLM counterparts. However, when it came to predicting future values, such as electricity temperatures or weather patterns, traditional autoregressive models held the edge, often delivering more accurate predictions with less computational overhead. This research highlights a crucial point: while LLMs offer a powerful new tool for analyzing time series data, they aren't a one-size-fits-all solution. The best approach depends on the specific task. Future research will likely focus on fine-tuning LLM architectures for time series analysis, exploring how to best combine the strengths of LLMs with existing techniques to achieve even more accurate and efficient insights from the ever-flowing streams of time-based information.