nixtla (@nixtlainc) / X

🎉 Announcing Nixtla Enterprise 2.0 🎉 Tl;DR: more models, domain expertise, reasoning capabilities, mcp interactions and optimized compute environments. We’re excited to share the next iteration of our enterprise offering. Starting today, companies can sign up for early

nixtla

@nixtlainc

Dec 4, 2025

Training a custom model for every cryptocurrency you want to forecast is tedious and impossible to scale. There are thousands of tokens. Market conditions shift constantly. By the time your model is trained, the opportunity is gone. TimeGPT changes this with zero-shot

nixtla

@nixtlainc

Dec 2, 2025

The same anomaly detection model can flag 89 or 505 anomalies, depending on one parameter. At 99% confidence, TimeGPT only flags extreme outliers. Drop it to 70%, and you catch subtle shifts that might indicate early warning signs. Neither is "correct." It depends on whether

When you’re forecasting many time series, each one has its own pattern. A single model won’t capture all that complexity, so you shouldn’t rely on one model across the entire dataset. But testing several models per series by hand is tedious and impossible to scale.

nixtla

@nixtlainc

Nov 20, 2025

Get interpretable neural forecasts with NHITS and NBEATSx decomposition 📈 Understanding forecast components (trend, seasonality, contributions) enables data scientists to explain model decisions to stakeholders and debug unexpected predictions. Traditional statistical methods

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