Guides, Tutorials and Insights on Time Series Forecasting

Nixtla Blog

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.

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.

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.

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.

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.

TimeGPT vs Snowflake - 50x Faster Forecasting with Better Accuracy

Discover SQL-native time series forecasting for Snowflake that's 10x faster than native tools. Nixtla provides state-of-the-art accuracy without Python, ML infrastructure, or complex setup.