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Applied Time Series Analysis and Forecasting with R Projects
MP4 | Video: AVC 1280x720 | Audio: AAC 48KHz 2ch | Duration: 3.5 Hours | Lec: 34 | 1.37 GB | Language: English + Sub .VTT



"Applied Time Series Analysis and Forecasting with R Projects"

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Applied Time Series Analysis and Forecasting with R Projects
MP4 | Video: AVC 1280x720 | Audio: AAC 48KHz 2ch | Duration: 3.5 Hours | Lec: 34 | 1.37 GB | Language: English + Sub .VTT





Use R to work on real world time series analysis and forecasting examples. Applied data science with R.



What you'll learn
Perform standard time series analysis tasks
Get ARIMA and exponential smoothing models in R
Do forecasting in R
Work with irregularly spaced time series
Model time series with trend and seasonality
Scrape stock data from yahoo finance
Import different types of time series data
Use automatic model selection in R
Select the best packages for time series analysis in R

Requirements
You need R/RStudio on your computer (add on packages will be outlined)
Basic R skills are required
Basic statistics skills would be helpful

Description
Welcome to the world of R and Time Series Analysis!

At the moment R is the leading open source software for time series analysis and forecasting. No other tool, not even python, comes close to the functions and features available in R. Things like exponential smoothing, ARIMA models, time series cross validation, missing data handling, visualizations and forecasts are easily accessible in R and its add on packages. Therefore, R is the right choice for time series analysis and this course gives you an opportunity to train and practice it.

So how is the course structured?

This is a hands on course with 3 distinct projects to solve! Each project has a main topic and a secondary topic. Both are discussed on real world data. In the first project you work with trending data, and as a secondary topic you will learn how to create standard and ggplot2 time series visualizations. The dataset for that project will be an employment rate dataset.

The second project with the German monthly inflation rates over the last 10 years shows how to model seasonal datasets. And you will also compare the models with time series cross validation.

In the third project you will connect R to yahoo finance and scrape stock data. The resulting data requires loads of pre-processing and cleaning including missing data imputation. Once we prepared the data, we will check out which weekday is the best for buying and selling the Novartis stock.

You should know some R to be able to follow along. There is for example the introduction to time series analysis and forecasting course. That course is more a step by step guide while this one is an applied and project based one. Both courses can be taken on their own, or you take a look at both and learn the subject from 2 different angles.

Who this course is for?
Data scientists, economists and all sorts of professionals working with time series datasets
Entrepreneurs and marketing experts interested in finding patterns in time series data
Students required to perform time series analysis
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Applied Time Series Analysis and Forecasting with R Projects

Applied Time Series Analysis and Forecasting with R Projects



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