A Complete Guide to Time Series Analysis & Forecasting in R

A comprehensive time series analysis and forecasting course using R

Forecasting involves making predictions. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments) or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an essential aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

What you’ll learn

  • Explore and visualize time series data..
  • Apply and interpret time series regression results..
  • Understand various methods to forecast time series data..
  • Use general forecasting tools and models for different forecasting situations..
  • Utilize statistical program to compute, visualize, and analyze time series data in economics, business, and the social sciences..
  • Use benchmark methods of time series forecasting..
  • Use methods for checking whether a forecasting method has adequately utilized the available information..
  • Forecast using exponential smoothing methods..
  • Stationarity, ADF, KPSS, differencing, etc..
  • Forecast using ARIMA, SARIMA, and ARIMAX..
  • Learn through plenty of rigorous examples and quizzes..

Course Content

  • Introduction –> 12 lectures • 43min.
  • Visualizing Time Series (Part 1) –> 6 lectures • 36min.
  • Visualizing Time Series (Part 2) –> 8 lectures • 48min.
  • Benchmark Methods (Part 1) –> 9 lectures • 44min.
  • Benchmark Methods (Part 2) –> 11 lectures • 51min.
  • Linear Regression (Part 1) –> 13 lectures • 1hr 5min.
  • Linear Regression (Part 2) –> 13 lectures • 48min.
  • Linear Regression (Part 3) –> 11 lectures • 52min.
  • Time Series Decomposition –> 16 lectures • 56min.
  • Exponential Smoothing –> 15 lectures • 1hr 11min.

A Complete Guide to Time Series Analysis & Forecasting in R

Requirements

  • A computer with R and Rstudio..
  • Basic knowledge of statistical terms, e.g., mean, median, mode, standard deviation, variance, etc..
  • Preferably, some knowledge of R programming..

Forecasting involves making predictions. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments) or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an essential aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

 

  • No prior knowledge of R or data science is required.
  • Emphasis on applications of time-series analysis and forecasting rather than theory and mathematical derivations.
  • Plenty of rigorous examples and quizzes for an extensive learning experience.
  • All course contents are self-explanatory.
  • All R codes and data sets and provided for replication and practice.

 

At the completion of this course, you will be able to

  • Explore and visualize time series data.
  • Apply and interpret time series regression results.
  • Understand various methods to forecast time series data.
  • Use general forecasting tools and models for different forecasting situations.
  • Utilize statistical programs to compute, visualize, and analyze time-series data in economics, business, and the social sciences.

You will learn

  • Exploring and visualizing time series in R.
  • Benchmark methods of time series forecasting.
  • Time series forecasting forecast accuracy.
  • Linear regression models.
  • Exponential smoothing.
  • Stationarity, ADF, KPSS, differencing, etc.
  • ARIMA, SARIMA, and ARIMAX (dynamic regression) models.
  • Other forecasting models.