Résumé
Time series prediction is critical in domains such as economics, industry, and agriculture. Real-world scenarios often involve challenges like low data frequency, high variability, and non-repetitive patterns. Traditional statistical models and machine learning approaches, including Long Short-Term Memory (LSTM) networks, underperform in low-data contexts due to overfitting risks, intensive training requirements, and the lack of benchmarks tailored to such scenarios. Large language models (LLMs) have emerged as tools for time series forecasting, leveraging their ability to generalize and capture temporal dependencies and patterns across datasets without requiring extensive task-specific feature engineering. This study investigates the potential of time series foundation models (TSFMs), specifically Lag-Llama and Chronos, on low-frequency datasets by comparing zero-shot prediction across one-step-ahead and multi-step-ahead approaches. Our findings evaluate predictive error, robustness, efficiency, and applicability, showing how TSFMs address these limitations and enhance forecasting in data-scarce scenarios.