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Time Series Analysis and Forecasting using R

learn Time series analysis, forecasting and business analytics with the perspective of a data scientist
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(100) Ratings
2,489 students
Created by EDUCBA Bridging the Gap
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What you'll learn

  • Methods of Forecasting and Steps in Forecasting
  • Problems in Forecasting and Simple Forecasting Methods
  • Simple and Multiple Regression and Time Series Decomposition
  • Exponential Smoothing and ARIMA models
This course includes:
4.5 total hours on-demand video
0 articles
0 downloadable resources
32 lessons
Full lifetime access
Access on mobile and TV
Certificate of completion
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Course content

Requirements

  • Basic knowledge in statistics, mathematics, programming
  • Basic knowledge of using R and Excel

Description

Learn how to effectively work around business analytics to find out answers to key questions related to business. We are using sophisticated statistical tools like R and excel to analyze data. This training is a practical and a quantitative course which will help you learn business analytics with the perspective of a data scientist. The learner of this course will learn the most relevant techniques used in the real world by data analysts of companies around the world.

The training includes the following;

  • Introduction to Forecasting

  • Models/Methods of Forecasting

  • Steps in Forecasting

  • Problems in Forecasting

  • Simple Forecasting Methods

  • Simple and Multiple Regression

  • Time Series Decomposition

  • Exponential Smoothing

  • ARIMA models

  • Conclusion

Time series in R is defined as a series of values, each associated with the timestamp also measured over regular intervals (monthly, daily) like weather forecasting and sales analysis. The R stores the time series data in the time-series object and is created using the ts() function as a base distribution.

How Time-series works in R?

R has a powerful inbuilt package to analyze the time series or forecasting. Here it builds a function to take different elements in the process. At last, we should find a better fit for the data. The input data we use here are integer values. Not all data has time values, but their values could be made as time-series data. The data consists of observations over a regular interval of time. It needs several transformations before it is modeled up.

Who this course is for:

  • Students, Marketing professionals, Market Researchers, Product Managers, Any person running a business
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