Comprehensive Time Series Exploratory Analysis

A deep dive into air quality data

Photo by Jason Blackeye on Unsplash

Here you are with a dataset indexed by time stamps. Your data might be about storage demand and supply, and you are tasked with predicting the ideal replenishment intervals for a strategic product. Or maybe you need to translate historical sales information into key actionable insights for your team. Perhaps your data is financial, with information about historical interest rates and a selection of stock prices. Maybe you are tasked with modelling market volatility and need to quantify monetary risk over an investment horizon. From social sciences and energy distribution or from healthcare to environmental studies. The examples are numerous. But what do these scenarios have in common? One, you have a time series task at hand. And two, you will certainly benefit from starting with a succinct yet comprehensive exploratory analysis.

Table of Contents

• The Goal of this Article
• Dataset Description
• Libraries and Dependencies
• Getting Started
• The Big Picture
• A Detailed View
• Missing Values
• Intermittency
• Seasonality