Do you want to create a time series model and use it for forecasting? Nearly everyone working in a quantitative field has to work with time series data. This sort of data has specific rules, functions and visualizations which you will learn in this course, Beginning Time Series Analysis and Forecasting with R. First, you'll learn about time series data, which is data captured along a timeline. That means time series data has a specific order (a timestamp) which allows different types of analysis and modeling. Next, you'll explore how these models can be used to create forecasts which are widely used in many fields ranging from finance to academia or medicine. R is the favorite tool among data scientists to do time series analysis. Knowing this, you'll finally touch on the variety of add on packages that were created especially for that purpose, most prominently the package “forecast” by J Hyndman. By the end of this course, you'll not only know about the underlying statistics of time series but also about models like ARIMA, exponential smoothing or simpler types of models. Of course you will use these models to create forecasts!
Martin is a trained biostatistician, programmer, consultant and data science enthusiast. His main objective: Explaining data science in a straightforward way. You can find his latest work over at: r-tutorials.com
Course Overview Hi guys, this is Martin Burger, and I welcome you to my course, Beginning Time Series Analysis and Forecasting with R. The time series side of data science is an immensely important one. Just think about stock prices, sales data, or MATs in bioavailability over time. Each of these fields work with time series and forecasting techniques. As a data scientist and biostatistician, I work with this sort of data on a regular basis; therefore, I know firsthand about the importance of this field. R is widely used for time series analysis. It offers an array of great add-on packages like the very popular forecast package. Now, in this course, you will see what a time series actually is. You will learn about its specific statistical characteristics, as well as their specific terminology. You will learn modeling and forecasting techniques. These are simple techniques like the mean method or the drift method, but also advanced techniques like ARIMA models. You will learn how to compare models and identify the most suitable one. You will see how to decompose time series into its single components. And you will, of course, use several helpful add-on packages. By the end of this course, you will be able to handle, format, interpret, and analyze time series data so that you can select suitable models and understand the results. I would categorize this course as a beginners plus course. If you're familiar with basic R code, you will be able to fully benefit from the course. Alright guys, I really hope you will enjoy this class. I'll see you inside.