"How Much Can Time-related Features Enhance Time Series Forecasting?"

Chronos teaches transformers to speak the universal language of numbers, making time series forecasting feel like casual conversation.

Chronos transforms time series data into tokens, enabling standard transformer models to perform forecasting without architectural changes. This simple yet effective approach achieves superior performance on both seen and unseen datasets.

-----

https://arxiv.org/abs/2412.01557v1

🤔 Original Problem:

→ Traditional time series forecasting methods require dataset-specific training or complex architectural modifications to handle numerical data, making it challenging to develop a unified, general-purpose forecasting model.

-----

🔧 Solution in this Paper:

→ Chronos converts time series values into discrete tokens through scaling and quantization, creating a fixed vocabulary that transformer models can process.

→ The framework uses existing transformer architectures (T5 family) without any modifications, treating time series prediction as a language modeling task.

→ Models are pretrained on a diverse collection of public datasets and synthetic data generated via Gaussian processes to improve generalization.

-----

💡 Key Insights:

→ Simple tokenization of numerical values can effectively bridge the gap between language models and time series forecasting

→ Synthetic data generation significantly improves model generalization capabilities

→ Zero-shot performance can match or exceed task-specific models without any fine-tuning

-----

📊 Results:

→ Outperforms traditional and deep learning methods on datasets from training corpus

→ Achieves comparable or superior zero-shot performance on unseen datasets

→ Successfully operates with relatively modest model sizes (20M to 710M parameters)