Time Series Analysis Explained: ARIMA, Prophet, and Handling Data Gaps

Explore the essentials of time series analysis with ARIMA and Prophet. Learn to handle missing data, understand seasonality, and improve forecasting accuracy.

Time series analysis is a key concept in data analytics, especially when dealing with data collected over time. Think of it like this: whether you're looking at stock prices, sales figures, weather patterns, or economic indicators, time series data is all around us.

In this article, we’re going to dig into the basics of time series analysis. We'll explore important components like trends (the general direction data is moving), seasonality (repeating patterns at certain times), and noise (random fluctuations).

We'll also look at popular forecasting methods such as ARIMA and Facebook Prophet.

Each week, I dive deep into Python and beyond, breaking it down into bite-sized pieces. While everyone else gets just a taste, my premium readers get the whole feast! Don't miss out on the full experience – join us today!

Whether you're just starting with time series analysis or looking to refresh your knowledge, this guide will give you a clear and thorough understanding.

Today I will cover all the important concepts to help you wrap your brain around Time series analysis and get started here today! I want to focus on key concepts like trends, seasonality and noise, while also introducing you guys to the two forecasting methods of ARIMA and Prophet.

This is all apart of my current series on Data Analytics where I started with an introduction into Data Analytics and have worked through stages of Exploratory Data Analytics into Machine Learning. This series is written for you guys, my readers!

If you are not already subscribed to my premium make sure you do as you get full access to these articles and all my code to follow along with!

Along with getting access to my Data Analytics series you also get exclusive access to a boat load more content such as monthly Python projects, weekly long form articles, 3 Randoms, & my complete archive!

I pour a lot of time into this stuff so please do consider joining premium as that really helps me keep going and shows that you are gaining value from my work.

Okay, enough small talk. Time to go into a new topic that looks daunting on the outside but after this read here today you will have a much better understanding and how you can hit the ground running!

👉 If you get value from this article, please help me out and leave it a ❤️. This helps more people discover this newsletter on Substack! Thank you so much!