Time series data—those streams of measurements taken over time—are everywhere, from stock prices to heartbeats. But unlocking the insights hidden within these complex sequences has always been a challenge. Could large language models (LLMs), known for their prowess with human language, hold the key to deciphering the language of time itself? New research explores this fascinating possibility with a technique called LLM-ABBA. It turns out that time series data, like human speech, has its own intricate semantic information. The problem is, LLMs can't directly understand numerical values in the way they understand words. LLM-ABBA bridges this gap by using symbolic approximation. Imagine converting a complex heartbeat pattern into a simplified symbolic sequence, like “aBbCbCA.” This sequence captures the essential features—the ups and downs, the rhythms and pauses—in a format LLMs can digest. The ABBA method, short for Adaptive Brownian Bridge-Based symbolic Aggregation, cleverly compresses and digitizes time series, turning them into these symbolic representations. Researchers tested LLM-ABBA on a variety of tasks, including classifying time series (like identifying different types of heartbeats), regression (predicting numerical values from time series), and forecasting future values. The results are promising. LLM-ABBA achieved state-of-the-art performance on many time series regression tasks and competitive results on classification. For forecasting, it showed comparable accuracy to specialized time series models, even though it uses a radically different approach. This suggests that LLMs, armed with the right tools, can indeed learn the “chain of patterns” within time series and make insightful predictions. While challenges remain, like mitigating errors in reconstructing the original data from the symbols and dealing with LLM “hallucinations,” LLM-ABBA opens exciting new avenues for understanding the hidden language of time. It brings us closer to a future where LLMs could analyze everything from medical data to financial markets with a deeper, more nuanced understanding of the temporal dynamics at play.