statsforecast

Discovering outliers, unusual patterns or events in your time series data has never been easier! In this tutorial, I’ll walk you through a step-by-step guide on how to detect anomalies in time series data using Python. You won’t have to worry about missing sudden changes in your data or trying to keep up with patterns that change over time. I’ll use website impressions data from Google Search Console as an example, but the techniques I cover will work for any time series data....

In today’s article, we’re going to explore the ins and outs of training a Holt-Winters model for forecasting multiple time series in Python. Holt-Winters is a very popular forecasting algorithm that can capture seasonality and trends in time series data through exponential smoothing. I’ll use StatsForecast, a scalable and easy-to-use Python library that can help you train a Holt-Winters model quickly and efficiently. You don’t need to be a programming wizard to get started with this library, and it can save you hours of coding time....