We have an exciting new update! 🚀 Introducing v2 of our TimeGPT API: Faster, Smarter, and More Powerful! 🚀 We’re thrilled 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. 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. 🚄 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!

Will any of this be available through the R package, nixtlar?

Hi @Vidar Ingason we're currently in the final stages for the CRAN submission. I'm not sure if it will have the SHAP values since those are also new, but we'll do our best to also include them, together with the features just announced.

Hi @Mariana Menchero, the feature I'm most interested in is the historical exogenous variables. Will that feature be part of the CRAN submission?

yes, we'll include that too.

Hi, great to hear about the updates. But can you briefly explain the working principle of "*Historical Exogenous Variables".* When future data of that variable is not there, how do you even take advantage of it in generating forecast? Does Nixtla automatically generate forecast of these historical features & then use it as future exogenous variables? Just curious to know

@Santosh Puvvada yes, when the future values of the exogenous variables are not available, TimeGPT extends them. The details of how this is done are proprietary, but TimeGPT tries to leverage this historical information to then generate the forecast for the target variable. We of course first recommend obtaining these future values. If this is not possible, you can also use TimeGPT to forecast them directly. This will allow you to have clarity on how they're expected to behave in the future. And in case you're not aware, here's a tutorial on exogenous variables in the v2 version.

ok, great, thanks a lot!

Great work, thanks @Mariana Menchero. I see in the tutorials that a single Historical Exogenous Variable may present different values according to each unique_ID at a given point in time. Let's say I have 3 different HEV for 3 unique_ID. Do I stack them in the same HEV column or do I create 3 separate HEV columns (which will therefore include NA value for 2 out of 3 ID variables) ? Hope I'm clear enough.