This document discusses using the R software environment for business forecasting. It provides an overview of important R packages and techniques for short and long term forecasting, including simple methods, regression, time series econometrics, and advanced multivariate techniques. The course teaches students how to import and analyze data in R, visualize results, and generate reports for presentation and writing using R Markdown. Mastering these skills allows businesses to make better strategic decisions using statistical forecasting tools.

1. Business Forecasting Using R
Working with real data to solve real world problem
The skill to forecast is very important in corporate strategy making. Using the power of modern software’s like R it is
possible to carry out good short term and long term forecasts.
R is a powerful software environment for statistical computing and graphics. R is extensively used by large corporate
houses, business schools and universities (like Stanford, Harvard, Princeton, Washington University, MIT etc.). R is free.
Step by step learning
 Introduction to R using RStudio: Explore important R packages, import data into R, elementary coding, working
with dataframe, data visualization, saving output in MS word and MS excel.
 Some simple forecasting methods: Using mean; naïve method; drift method
 Forecasting with regression: Correlation matrix to filter variables; Simple regression modelling; multiple
regression modelling; interpretation of output generated by R; assessing overall accuracy of the models
 Forecasting with economic indicators: Studying sources and definitions of business cycle indicators of Indian
economy; important indicators to watch; identification of coincident, lagging and leading indicators for some
important industries and economy
 Forecasting with time series econometrics: Use of auto correlation and partial auto correlation function; Auto
Regression (AR), Moving Average (MA) and Auto Regression Intigrated Moving Average (ARIMA) models;
Programming for automatic model selection; Identify Seasonality; generate deseasonalised data series;
Forecasting using seasonal, cyclical and trend components
 Forecasting with advance multivariate techniques: Vector Auto Regression (VAR); Error correction models;
Principal component regression (big data technique)
 Model evaluation: Overview of forecast accuracy test statistics; Test statistics selections at different situations in
real world.
 Report writing and presentation using R-Markdown: Prepare high quality presentation and generate automatic
reports in MS word with text, tables and charts.
Pinaki M Mukherjee