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Detect production anomalies in minutes with TimeGPT's rolling forecast 🚨 Traditional anomaly detection requires retraining on entire datasets, making real-time monitoring impossible. This leads to delayed responses when critical system failures or data quality issues occur in…

Automate NeuralForecast hyperparameter tuning with Ray Tune ⚡️ Manual hyperparameter tuning for neural forecasting models can be time-consuming and requires deep expertise in model architectures. NeuralForecast's Auto models (AutoNHITS, AutoTFT, AutoLSTM) automatically search…

Spotted at Nike World Headquarters 👟 where Nixtla appeared on the big screen during a forecasting session. Moments like this remind us how far the work on better forecasting and modeling continues to travel. #Nixtla #Forecasting #DataScience #Nike

Add event context to your forecasts with TimeGPT categorical variables ⚡ Categorical variables capture discrete event types (promotions, holidays, seasons) that drive demand patterns in time series data. Without them, models treat all time periods uniformly, missing the signal…

Validate time series models the right way with cross-validation ⚡ Cross-validation tests your model on multiple time windows to ensure it generalizes well to future data. However, setting up temporal splits and running validation loops is slow and complex. StatsForecast makes…

Improve forecast accuracy with TimeGPT's exogenous variables 📊 Including external variables like weather, marketing spend, or holidays helps capture the real drivers behind your forecasts, reducing prediction errors. This approach lets you leverage domain knowledge and account…

🚀 Introducing the TimeGPT-2 family: next-generation time-series foundation models Today, we’re announcing the private preview of TimeGPT-2 Mini, TimeGPT-2, and TimeGPT-2 Pro, built for reliable, enterprise-grade time series forecasting. The TimeGPT-2 family is optimized for…

Handle noisy data and outliers with Huber loss 📈 Standard loss functions are sensitive to outliers, causing models to overfit to anomalies and produce unstable forecasts. Huber loss in NeuralForecast provides robust training that's less sensitive to outliers while maintaining…

Build an adaptive monitoring system with rolling forecasts 🎯 Production metrics drift with growth and seasonality, which makes static alerts unreliable. For example, if you set an alert at 5k when your baseline later moves to 10k, you will be flooded with useless alerts, and…

The community asked, and Nixtla responded! ✅ We’re excited to announce a time series webinar to help you get started in the VN2 inventory planning competition. In this live webinar, Nixtla's experts will share concrete tips and code examples on how to easily build…

Time series decomposition breaks down forecasts into constituent components like trend and seasonality. This reveals underlying patterns and improves understanding. NHITS (Neural Hierarchical Interpolation for Time Series) advances this with hierarchical multi-scale…

Handle forecast uncertainty with StatsForecast prediction intervals! Point forecasts provide only a single predicted value without any uncertainty information. You can't assess how confident the model is in its prediction, making risk assessment impossible. StatsForecast…

Our documentation just got a refresh! It’s now easier to explore and find what you need. Come see for yourself! 🔗 Docs: bit.ly/3IE3I1C

(Shapley) Explanations are now available in NeuralForecast v3.1.0! 🎉 The latest release adds SHAP-powered explanations to univariate forecasting models in NeuralForecast, making it clear which features drive each forecast via the explain() method. The feature contribution…

A new benchmark tests 9 models across 21 crypto assets on daily and hourly data. Two standout results: 🔹 Accuracy: Fine-tuned TimeGPT (no variables) leads on average across daily and hourly 🔹 Speed: Zero-shot TimeGPT is 10 times faster than deep nets, enabling rapid iteration…

When AI in healthcare is discussed, the focus often goes to diagnostics or patient chatbots. While those are valuable, many of the most consistent cost pressures arise in operations and supply chains. For example: 🔹 Stockouts can lead to emergency orders at higher prices. 🔹…

We’re proud to be ranked by G2, the world’s largest software marketplace, where real users review and rank business tools, as: 🏆 Highest Performer 🍰 Easiest to Use Thanks to everyone who has supported us on this journey to make time series forecasting faster, more accurate,…

Eliminate target transformation errors with MLforecast (automated preprocessing)! Time series forecasting often requires target variable transformations. For example, you may want to apply a difference transformation to make the target variable stationary. But relying on manual…

Want to land a job as a Machine Learning Engineer at OpenAI or DoorDash? Turns out brushing up on your Python isn’t enough… Step 1: Know TimeGPT 😉 (it’s literally in their job descriptions) Step 2: Apply Step 3: Show off those forecasts 🚀

Discover which automated time series features drive model performance with MLforecast! Which features from your automated time series engineering actually improve predictions? Without visibility into feature importance, you don't know whether your automated feature engineering…