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The Forecast Says Zero. The Warehouse Says Otherwise.

The Forecast Says Zero. The Warehouse Says Otherwise.

A forecast that predicts zero every month can still win on accuracy metrics and still be useless to the business. Three practical fixes for intermittent demand forecasting: Croston/TSB, better metrics, and forward looking signals.

intermittent demandCroston
Your MAE Went Down and You Still Lost Money

Your MAE Went Down and You Still Lost Money

Squared error recovers the mean, absolute error the median. Neither is the number you should act on. The newsvendor critical ratio and pinball loss explain why.

probabilistic forecastingquantile forecasting
Categorical Exogenous Variables in NeuralForecast: Embeddings, Configuration and a Hands On Example

Categorical Exogenous Variables in NeuralForecast: Embeddings, Configuration and a Hands On Example

Learn how NeuralForecast turns categorical exogenous variables into learned embeddings, why that beats one hot or ordinal encoding, and how to configure it for your own models.

neuralforecasttime-series
Beyond Accuracy: What Makes a Good Forecast?

Beyond Accuracy: What Makes a Good Forecast?

Accuracy is essential, but a good enterprise forecast must also support real decisions, use the right information, earn trust, and operate reliably at scale.

TimeGPTNixtla Engine
TimeGPT Is Now on Microsoft Foundry: From Deploy to First Forecast

TimeGPT Is Now on Microsoft Foundry: From Deploy to First Forecast

TimeGPT is now available through Microsoft Foundry, letting Azure customers deploy it inside their own subscription, billed as standard Azure consumption, without a separate vendor review.

Microsoft FoundryAzure Marketplace
Still using Prophet or AutoARIMA for 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.

TimeGPTDatabricks
Forecasting 145,000 Time Series in 7 Minutes with TimeGPT

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.

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

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

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-2Nixtla MCP
TimeGPT 2.1: The Next Generation of Foundation Models for Time Series Forecasting

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.

TimeGPT-2foundation models
Supercharge Your Sales Forecasts: A Complete Guide to Exogenous Variables in MLForecast

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.

mlforecasttime-series
Forecasting Championship Results using Time Series and Nixtla

Forecasting Championship Results using Time Series and Nixtla

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

StatsForecastforecasting
Automatic Model Selection with StatsForecast for Time Series Forecasting

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.

StatsForecastautomatic model selection
Damage Detection in Engineering Structures Using Nixtla

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.

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

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.

TimeGPT-2foundation models
Anomaly Detection for Cloud Cost Monitoring with Nixtla

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.

TimeGPTanomaly detection
Performance Evaluation of Anomaly Detection through Synthetic Anomalies

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.

TimeGPTanomaly detection
Anomaly Detection in Time Series with TimeGPT and Python

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.

TimeGPTanomaly detection
Automated Time Series Feature Engineering with MLforecast

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.

MLforecastautomated feature engineering
Effortless Accuracy Unlocking the Power of Baseline Forecasts

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.

baseline forecastingstatsforecast
Eliminate Manual ARIMA Tuning Using StatsForecast AutoARIMA Automation

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.

StatsForecastAutoARIMA
Time Series Frequency Modelling with Fourier Transform and TimeGPT-1

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.

TimeGPTFourier Transform
Understanding Intermittent Demand

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.

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

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.

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

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.

anomaly detectionoptimal baseline subtraction
Savitzky Golay Filtering for Time Series Denoising

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

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.

TimeGPTPolars
TimeGPT vs Snowflake - 50x Faster Forecasting with Better Accuracy

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.

TimeGPTSnowflake