Blog | Nixtla | Time Series Forecasting

Still using Prophet or AutoARIMA for forecasting?

We benchmarked TimeGPT-2.1 against Databricks AutoML on electricity, weather, and retail demand datasets, comparing accuracy, runtime, and per-series win rates. TimeGPT-2.1 improved MAE, RMSE, MAPE, and WAPE across all three benchmarks.

Forecasting 145,000 Time Series in 7 Minutes with TimeGPT

TimeGPT forecasts all 145,063 Wikipedia web traffic series in 6.6 minutes, beating a top Kaggle statistical baseline by 18.8% on SMAPE, with zero feature engineering.

TimeGPT in Snowflake Just Got a Full Upgrade: Anomaly Detection, Explainability, and a One-Command Install

The Nixtla Snowflake integration is now part of the official nixtla package. Run forecasting, anomaly detection, SHAP-based explainability, and evaluation, all from pure SQL, all inside Snowflake.

Nixtla Enterprise Expands with Leading Foundation Models, MCP, and Agentic Capabilities

This release introduces three major capabilities that together expand Nixtla from a single-model offering into a full time series intelligence platform

TimeGPT 2.1: The Next Generation of Foundation Models for Time Series Forecasting

Announcing the private preview of TimeGPT-2.1, the first multivariate model in the TimeGPT family.

Supercharge Your Sales Forecasts: A Complete Guide to Exogenous Variables in MLForecast

Learn how to incorporate external factors like prices, promotions, and calendar patterns into your time series forecasts using MLForecast's exogenous variables.

Forecasting Championship Results using Time Series and Nixtla

Learn how to forecast championship standings using Nixtla's StatsForecast library.

Automatic Model Selection with StatsForecast for Time Series Forecasting

Stop testing statistical models manually. Use StatsForecast to automatically fit AutoARIMA, AutoETS, AutoCES, and AutoTheta models, then select the best performer for each series through cross-validation.

Damage Detection in Engineering Structures Using Nixtla

Learn how to detect cracks and structural damage using Nixtla's Anomaly Detection pipeline while accounting for temperature-induced variations in sensor data.

TimeGPT 2: The Next Generation of Foundation Models for Time Series Forecasting

Announcing the private preview of TimeGPT-2 Mini, TimeGPT-2, and TimeGPT-2 Pro, enterprise grade foundation models with up to 60% accuracy improvement, built for mission-critical time series forecasting.

Anomaly Detection for Cloud Cost Monitoring with Nixtla

Learn how to build a synthetic cloud cost dataset and use Nixtla's algorithms to detect spikes, drifts, and level shifts. This approach helps teams monitor performance and prevent unexpected billing surprises.

Performance Evaluation of Anomaly Detection through Synthetic Anomalies

Discover how to find the minimum detectable anomaly in absence of a ground truth labelled dataset using synthetic anomalies.

Anomaly Detection in Time Series with TimeGPT and Python

Discover how to use TimeGPT for scalable, accurate anomaly detection in Python. Includes real-world time series, exogenous variables, and adjustable confidence levels.

Automated Time Series Feature Engineering with MLforecast

Replace hours of custom feature engineering code with MLforecast's automated lag features, rolling statistics, and target transformations for faster, more reliable time series forecasting.

Effortless Accuracy Unlocking the Power of Baseline Forecasts

Understand what are baseline forecasts, why they are important and learn to create them easily with Nixtla's statsforecast package.

Eliminate Manual ARIMA Tuning Using StatsForecast AutoARIMA Automation

Eliminate weeks of manual ARIMA parameter tuning with StatsForecast's AutoARIMA. Automatically select optimal model parameters for 50+ time series with confidence intervals in under 30 minutes.

Time Series Frequency Modelling with Fourier Transform and TimeGPT-1

Discover how to decompose your time series in multiple components with Fourier Transform and model each component with TimeGPT-1.

Understanding Intermittent Demand

Learn how to forecast intermittent demand using Python and Nixtla's TimeGPT. This step-by-step guide covers handling sparse time series, fine-tuning, and using exogenous variables to improve accuracy.

Long Term Mid Term and Short Term Forecasting with Polynomial Regression AutoARIMA and TimeGPT-1

Learn how to match forecasting models to your time horizon for better accuracy. Compare polynomial regression for long-term trends, AutoARIMA for mid-term cycles, and TimeGPT-1 for short-term predictions using real currency exchange data. Includes code examples for multi-horizon forecasting strategies.

Simple Anomaly Detection in Time Series via Optimal Baseline Subtraction (OBS)

Discover how to detect anomalies using Optimal Baseline Subtraction and enhance your forecasts with Nixtla’s TimeGPT on real-world weather data.

Savitzky Golay Filtering for Time Series Denoising

Denoise your time series with polynomial smoothing using the Savitzky-Golay filter

Production-Ready Forecasting Pipeline with TimeGPT and Polars

Learn how TimeGPT's native DataFrame compatibility lets you leverage Polars' blazing-fast performance for time series forecasting without data conversion overhead.

TimeGPT vs Snowflake - 50x Faster Forecasting with Better Accuracy

Discover SQL-native time series forecasting for Snowflake that's 50x faster than native tools. Nixtla provides state-of-the-art accuracy without Python, ML infrastructure, or complex setup.

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