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Introduction to Time Series Analysis and Forecasting in R

 

Introduction to Time Series Analysis and Forecasting in R

GETTING STARTED

Introduction to Time Series Analysis and Forecasting in R Work with time series and all sorts of time related data in R - Forecasting, Time Series Analysis, Predictive Analytics
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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?



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