Vitor Cerqueira

How to detect time series change points using Python Vitor Cerqueira · Follow Published in Towards Data Science · 7 min read · 8 hours ago — Photo by Robby McCullough on Unsplash In this article, we: Define what time series structural changes are and what distinguishes them from outliers. Overview different types of structural changes. Explore change point detection methods, such as CUSUM, using the kats and ruptures packages. Introduction Stationarity is a central

Understanding, detecting and replacing outliers in time series Vitor Cerqueira · Follow Published in Towards Data Science · 6 min read · 2 hours ago — Photo by Milton Villemar on Unsplash In this post, we’ll explore: Different types of time series outliers Prediction-based and estimation-based methods for detecting outliers How to deal with unwanted outliers using replacement Types of Outliers Outliers are observations that deviate significantly from normal behavior. Time series can exhibit outliers

6 graphics to explore your time series Vitor Cerqueira · Follow Published in Towards Data Science · 7 min read · 10 hours ago — Photo by Alex Litvin on Unsplash Visualization is a quick and effective way of getting insights from your data. This article provides a step-by-step guide for exploring a time series using graphics. We’ll use 6 different plots to uncover different aspects of a time series. We’ll focus on Python’s plotnine,

Unsupervised change detection using reference windows, with a Python example Vitor Cerqueira · Follow Published in Towards Data Science · 6 min read · 14 hours ago — Photo by Chris Czermak on Unsplash In a previous article, we explored the basics of concept drift. Concept drift occurs when the distribution of a dataset changes. This post continues to explore this topic. Here, you’ll learn how to detect concept drift in problems where you don’t