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.
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.