What you'll learn
use R to perform calculations with time and date based data
create models for time series data
use models for forecasting
identify which models are suitable for a given dataset
visualize time series data
transform standard data into time series format
clean and pre-process time series
create ARIMA and exponential smoothing models
know how to interpret given models
identify the best time series libraries for a given problem
compare the accuracy of different models

Requirements
computer with R and RStudio ready to use
interest in statistics and programming
time to solve the exercises
basic knowledge of R (course R Base)
NO advanced statistics or maths knowledge required

Description
Understand the Now - Predict the Future!

Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to

see patterns in time series data
model this data
finally make forecasts based on those models
Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!

What will you learn in this course and how is it structured?
You will learn about different ways in how you can handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package and especially the lubridate package.

You will learn how to visualize, clean and prepare your data. Data preparation takes a huge part of your time as an analyst. Knowing the best functions for outlier detection, missing value imputation and visualization can safe your day.

After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests.

Then you will see how different models work, how they are set up in R and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied with plenty of exercises.

Where are those methods applied?
In nearly any quantitatively working field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied.

Is it hard to understand and learn those methods?
Unfortunately learning material on Time Series Analysis Programming in R is quite technical and needs tons of prior knowledge to be understood.

With this course it is the goal to make understanding modeling and forecasting as intuitive and simple as possible for you.

While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with time data on a regular basis can benefit from this course.

How do I prepare best to benefit from this course?
It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (course R Basics).

What R you waiting for?

Who this course is for?
this course is for people working with time series data
this course is for people interested in R
this course is for people with some beginner knowledge in both R programming and statistics
this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science
generally if you have time series data on your table and you do not know what to do with it, take this course!
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