From LLMs to Time Series: GenAI's Impact on Gradient Boosting Models

The rise of Generative AI (GenAI) and Large Language Models (LLMs) has captivated the world, transforming various fields. Initially focused on text sequences, these technologies are now being applied to diverse data formats, including time series forecasting. This domain benefits from blending domain knowledge with the task-agnostic potential of LLMs, promising previously unattainable results.

Deep Learning and Time Series Forecasting

Deep learning in time series forecasting isn't new. Transformers, introduced in "Attention is All You Need," have found applications here. The challenge is adapting these models to handle numerical sequences accurately, unlike text-based applications where reordering words doesn't disrupt meaning.

Zero-Shot Forecasting with Standard Machine Learning Models

• Gradient Boosting Models: Known for fast training, minimal pre-processing, and explanatory power, these models are essential in time series forecasting. Linear baseline methods also serve as robust alternatives or ensemble candidates.

Transfer Learning in Time Series Forecasting

The goal is forecasting unseen time series using a model trained on different data, without retraining. This transfer learning approach is effective with limited data or long model development times, provided training and target domains are similar.

• Practical Application: Download and store M3 and M4 datasets, then train a global forecasting model on M4. Evaluate it on M3 using a recursive instance, capturing common patterns and enhancing generalization.

As LLMs rise in time series forecasting, simpler methods often achieve similar results with less effort.

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