Large Language Models Transform Time-Series Analysis with Advanced Pattern Detection and Automated Forecasting

Large Language Models revolutionize time-series analysis by automatically detecting patterns, spotting anomalies, and generating forecasts through advanced prompt engineering, while streamlining preprocessing tasks like stationarity testing and seasonal decomposition to transform how analysts approach temporal data prediction.

• Large Language Models combined with smart prompt engineering revolutionize time-series analysis by spotting patterns, detecting anomalies, and making forecasts through proven strategies like patch-based prompting and zero-shot contextual instructions
• Key preprocessing techniques include stationarity testing, autocorrelation analysis, and seasonal decomposition, with LLMs providing automated code generation and interpretation for ACF/PACF plots and STL decomposition
• LLMs excel at anomaly detection through direct prompting and forecasting-based approaches, while also automating feature engineering across lag features, rolling windows, time-based variables, and external factor integration