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Hi guys… in this video I have talked about the theory of ARIMA (Auto regressive integrated moving average) time series forecasting methodology. I have tried to explain its component like ACF, PACF and lagged difference with the help of simple example to that you can understand their functioning in ARIMA process.

Theory of Arima time series forecasting methodology

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As an aspiring analytics and data science professional know that time series forecasting is most definitely part of the 20% of analytics that drive 80% of ROI.

Univariate times series forecasting is a wildly useful skill that every business uses (and I mean every business).

The single best resource I know for learning time series forecasting is the work of Professor Rob J Hyndman. The professor is the author of the mighty forecast package in R and also has a most excellent free introductory text on time series forecasting.

Highly recommended. The professor is a genius, IMHO. Links below.

Stay healthy and happy data sleuthing!

Here are some valuable links:

* Link to free text:

* Great talk from the professor at a R user group meeting:

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Video from this meetup:

Learn Data Science by Doing Kaggle Competitions: Web Traffic Forecasting

Thursday, Jul 26, 2018, 6:00 PM

SFU VentureLabs Harbour Centre – 11th Floor
555 W Hastings St Vancouver, BC

79 Data Scientists Went

We meet every two weeks to learn more about data science by discussing Kaggle competitions ( If you want to get better at data wrangling, feature engineering, model selection or just want to have fun solving non-trivial data science problems, this is the right group to join! This time we will discuss the competition Web Traf…

Check out this Meetup →

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Time Series Analysis | Time Series Forecasting | Time Series Analysis in R | Ph.D. (Stanford)

Time Series Analysis is a major component of a Data Scientist’s job profile and the average salary of an employee who knows Time Series is 18 lakhs per annum in India and 0k in the United States. So, it becomes a necessity for you to master time series analysis, if you want to get that high-profile data scientist job.

Visit Great Learning Academy, for free access to full courses, projects, data sets, codebooks & live sessions:

This full course on Time Series Analysis will be taught by Dr Abhinanda Sarkar. Dr Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. He is ranked amongst the Top 3 Most Prominent Analytics & Data Science Academicians in India.

He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc).

Thus, keeping in mind, the importance of time series analysis, we have come up with this Full-course:

These are the topics covered in this full course:
• Types of statistics – 6:18
• What is Time Series Forecasting? – 21:12
• Components of Time Series – 55:23
• Additive Model and Multiplicative Model in Time Series – 1:16:48
• Measures of Forecast Accuracy – 3:04:55
• Exponential Smoothing – 3:47:50

Time Series Analysis explanation :

Here is a list of our other full course videos:

Probability and Statistics Full Course:

Machine Learning with Python:

Tableau Training for Beginners:

Python for Data Science:

Hadoop Full Course:

Statistics for Data Science :
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