Data & Analytics


Complete Time Series Data Analysis Bootcamp In R

Learn How To Work With Time Series/Temporal Data Using Statistical Modelling & Machine Learning Techniques In R

4.37 (308 reviews)


5.5 hours


Aug 2021

Last Update
Regular Price

What you will learn

Implement Common Data Cleaning And Visualization Techniques In R

Be Able To Read In, Pre-process & Visualize Time Series Data

The Basic Conditions Time Series Data Must Fulfill & How To Check For These

Model Time Series Data To Forecast Future Values

Use Machine Learning Regression For Forecasting Future Values

Detect Sudden Changes In The Values During A Given Time Period



This course is your complete guide to time series analysis using R. So, all the main aspects of analyzing temporal data will be covered n depth..

If you take this course, you can do away with taking other courses or buying books on R based data analysis.  

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in in analyzing time series data in R, you can give your company a competitive edge and boost your career to the next level.


Hey, my name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources  using data science related techniques and i have produced many publications for international peer reviewed journals.

 Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .

So, unlike other R instructors, I dig deep into the data science features of R and gives you a one-of-a-kind grounding in data science related topics!

You will go all the way from carrying out data reading & cleaning  to to finally implementing powerful statistical and machine learning algorithms for analyzing time series data.

Among other things:

  • You will be introduced to powerful R-based packages for time series analysis.

  • You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for time series data.

  • & you will learn to apply these frameworks to real life data including temporal stocks and financial data.  


You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using REAL DATA obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.

After taking this course, you’ll easily use the common time series packages in R...

You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.

We will work with real data and you will have access to all the code and data used in the course. 



Complete Time Series Data Analysis Bootcamp In R
Complete Time Series Data Analysis Bootcamp In R
Complete Time Series Data Analysis Bootcamp In R
Complete Time Series Data Analysis Bootcamp In R


INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Introduction to the Course

Data and Scripts For the Course

Installing R and R Studio

Read in CSV & Excel Data

Remove Missing Values

More Data Cleaning

Exploratory Data Analysis(EDA): Basic Visualizations with R

Start With Time Series Data

Works With Dates in R

Pre-Processing Data With Times

Visualize Temporal Data in R

Components of Time Series Data

Moving Averages (MA) For Visualizing a Trend/Pattern

Detecting Significant Trend

Other Ways Of Identifying Trend in Time Series Data

Visualize Monthly Temporal Data

Identify Cyclical Behavior with Fourier Transforms

STL Decomposition

Work With Seasonality

Important Pre-Conditions of Time Series Modelling

Is My Time Series Stationary?

Differencing: Make A Non-Stationary Time Series Stationary

Make the Mean & Variance Constant

Seasonal Differencing

Detrending Time Series With Linear Regression

Detrending Time Series With Mean Subtraction

Time Series Based Forecasting

Simple Exponential Smoothing for Short Term Forecasts

Other Basic Forecasting Techniques

Moving Averages (MA) For Forecasting

Simple Moving Average

Theta Lines For Forecasting

Forecasting On the Fly

Linear Regression For Predicting Values As a Function of Time

Linear Regression For Forecasting With Trend & Seasonality


Weekly Lags

Lagged Regression

Automatic ARIMA Model Fitting and Forecasting

Automatic ARIMA With Real Life Data

ARIMA With Fourier Terms

BATS For Forecasting

Machine Learning Techniques For Time Series Data

Linear Regression With "timetk"

Linear Regression On Real Data

Machine Learning Regression Models for Non-Parametric Data For Forecasting

XGBoost For Time Series Forecasting

Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)

Neural Network for Forecasting

RNNs With Temporal Data

Evaluate the Performance of an RNN Model

Detecting Sudden Changes/Major Events

Detect An Anomaly in Time Series Data

Breaks For Additive Season and Trend (BFAST) For Time Series in R

Structural Change Detection

Structural Changes in Forex Regime


Steven31 July 2020

Dr. Singh's courses are always very, very good. As a professional forecaster, this is a review for me. However, I wanted to make sure I have a good basis in R for future time series use. I look forward to working on this code as the course progresses! Thank you Dr. Singh, please keep up your great work.

AVINASH12 May 2020

the codes are good to learn something new in this course but for few topics there is no data available

Shivani7 May 2020

The explanation of the concept is very poor. The parameters of the function are not explained in detailed manner which creates a gap in understanding of the concept and next video.

Raymond7 February 2020

The course was very good. It reinforced my knowledge of R and provided me tips and knowledge regarding how to best approach a time series analysis problem. I also gained insights on using other approaches and frameworks such as machine learning and h2o. I will most likely go back and review a few sections in more detail to make sure I understand the code. Overall I found the material useful and applicable to the work I am currently doing. There were some minimal annoyances such as code files with ".txt" extensions in the course content. I needed to save them as ".R" files to take full advantage of "R" Studio features but, I consider that minor.

Yannis11 January 2020

The course needs to be updated. A few of the packages (such as HEAT) do not exist any more. Also some of the filenames are wrong. I found I could use a bit more explanation as to why the instructor picked the syntax/process she did. On the positive side the course does contain a plethora of examples that the student can go through and work out. I know R but I found I had to go to Google and understand a few things regarding the syntax used in the scripts.

Robert27 December 2019

Excellent presentation. The material is well organized and explained very well in clear and concise terms.

Nichola10 December 2019

There are some errors in the course and concepts are not explained clearly. It's necessary to google yourself as you go along with the course. As the complexity increases the amount of explanation decreases.

Mayur6 November 2019

It was good experience to learn new things. but sometimes i feel more explanation is needed to understand the concepts. Thank you

Titu7 October 2019

The course is of good content. I am finding great value. I have one suggestion. The transcript should be in paragraphs. Currently the sentences are broken and it is difficult to read coherently. very impressive and targeted delivery.

Utkarsh5 October 2019

The course introduced me towards some additional methods for understanding & predicting time series data. However, the explanation provided in the lectures is pretty limited and rather focused on just the execution part. I was still able to understand the content (for most part) as I had some previous background on time series forecasting in R. For someone new, it would be kind of difficult to understand why we are doing certain steps.

Mahender25 June 2019

Its starts by focussing on classical time series models before moving to more esoteric data science techniques

Marko18 May 2019

This is a very practical course with many examples and useful techniques. A lot of helpful packages are introduced. In the end, one can start to work with time-series data. To go even further (theoretical part), additional research is recommended.

Shamim18 May 2019

Pathetic narration. It seems that the codes are generated by another person and the narrator has little idea on the subject.

Jibon22 March 2019

I am very excited because it is a good match. But I need more practical experience to have a better understanding of the course.

Viluva18 March 2019

Wonderful course! A better understanding of how to undertake time-series analysis on real world problems


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