Introducing v2 of our TimeGPT API: faster, smarter and more powerful

We’re excited to unveil the latest release of our API—v2, packed with incredible improvements driven by the community’s feedback. The nixtla API lets you seamlessly connect with TimeGPT. Just pip install the newest version of our SDK (v0.6.0), nixtla, and you will have access to these new features.

You can install it with: pip install nixtla

This is a big release with a lot of new features. With v2, we’ve focused on what matters most: speed, scalability, flexibility, and precision. Whether you’re working on anomaly detection, forecasting, or cross-validating TimeGPT, these enhancements will enable you to achieve better results, faster.

⚡ Unmatched Speed Improvements

One of the standout upgrades in v2 is the dramatic increase in computational performance. We’ve fine-tuned our algorithms and optimized our infrastructure, delivering staggering results: we can detect anomalies 8.9x faster, forecast with exogenous variables 10x, and cross-validation 6x faster than the v1 of our API.  These speedups aren’t just numbers—they represent a huge leap in efficiency, allowing you to run complex analyses in a fraction of the time. This is especially crucial in production environments where time-to-insight is key. ⏱️

🌐 1 Billion Time Series in 6 Hours

But that’s not all. With v2, we’ve shattered previous limits. In our latest experiment, we successfully forecasted 1 billion time series in just 6 hours. This unprecedented capability sets a new standard for scalability in time series forecasting, empowering organizations to handle massive datasets with unparalleled speed. 🚀

📊 Advanced Handling of Exogenous Variables

We’ve also introduced a highly requested feature: the ability to distinguish between future and historical exogenous variables.

Why is this important?

• Historical Exogenous Variables: You can now leverage past data to boost the accuracy of your models, even when future data isn’t available. This is crucial for making reliable forecasts based on incomplete information. 🔍  
• Future Exogenous Variables: When future data is available, you can fine-tune your forecasts even further, giving you a predictive edge. This dual approach enables more robust and adaptable models, helping you better anticipate trends and anomalies. 📈

🔍 Enhanced Model Explainability with SHAP Values

In v2, we’ve also integrated SHAP values to enhance model interpretability. SHAP values allow you to understand the impact of each feature on TimeGPT’s predictions, providing deeper insights into the decision-making process. This is particularly valuable for model explainability and trust, especially in critical applications. 🧠

🛠️ New Integration with Polars

In addition to these improvements, we’ve added support for Polars—a lightning-fast DataFrame library. With Polars, you can process large datasets more efficiently, making it easier to manage and manipulate your time series data. This perfectly complements our existing integrations with Dask, Ray, Spark, and Pandas.

Why Polars?

• Speed: Polars is built for performance, especially with large datasets. ⚡
• Memory Efficiency: It uses less memory, making it ideal for big data applications. 💾
• Parallelism: Polars automatically parallelizes operations, speeding up your data processing workflows. 🚄

‍

See the full set of changes in the nixtla 0.6.0 release notes.

What This Means for You

We’ve listened to your feedback and made the changes you need to push the boundaries of what’s possible with time series forecasting.

We’re eager to hear your thoughts and continue improving.

💙 Happy forecasting!