Sales Forecast of Automobile Cars using R programming.

Objectives

• To forecast the sales made by an an automobile company every month.
• To show the use of ARIMA model in forecasting.

Method:

The data used to forecast were from the total sale of bolero cars for each end of the month from 2013 to
2014. The statistical forecasting method used is the ARIMA time series with the regression model.

Step 1:

• Plot tractor sales data as time series.

Step 2:

• Difference data to make data stationary on mean (remove trend).
• This to remove the upward trend through 1st order differencing the series using the following formula: Y_{t}^{'}=Y_t-Y_{t-1}

Step 3:

• log transform data to make data stationary on variance.
• The following equation represents the process of log transformation mathematically: Y_{t}^{new}=log_{10}(Y_t)

Step 4:

• Difference log transform data to make data stationary on both mean and variance.
• Formula: Y_{t}^{new'}=log_{10}(Y_t) -log_{10}(Y_{t-1})

Step 5:

• Plot ACF and PACF to identify potential AR and MA model.
• Autocorrelation factor (ACF) and Partial autocorrelation factor (PACF) plots are used to identify patterns in the data obtained from step 4 which is stationary on both mean and variance.
• The idea is to identify presence of AR and MA components in the residuals.

Step 6:

• Identification of best fit ARIMA model.
• Auto arima function in forecast package in R helps us identify the best fit ARIMA model on the fly.
• The best fit model is selected based on Akaike Information Criterion (AIC) , and Bayesian Information Criterion (BIC) values.
• The idea is to choose a model with minimum AIC and BIC values.

Step 7:

• Forecast sales using the best fit ARIMA model.
• Also don't forget to plot ACF and PACF for residuals of ARIMA model to ensure no more information is left for extraction.

Results:

• With a seasonable ARIMA model, the prediction plot of sales was obtained for the first half of year 2015.
• Click here to view the results and plots obtained according to the steps stated above.

Conclusions:

• ARIMA time series are useful models to predict the sales of automobile cars for this company. From this project, we can conclude that ARIMA and Regression models can be used by other businesses for planning.

Also you can click here to view the data dump for years 2013-2014.

Software Requirements

• R

  To install R for your operating system click here.

• R Studio

  To install R Studio for your operating system click here.

Package Requirement

• Forecast

To install a package to your computer

• Method 1: Install from source

  Download the add-on R package, say mypkg, and type the following command in Unix console to install it to /my/own/R-packages/:

  $ R CMD INSTALL mypkg -l /my/own/R-packages/

• Method 2: Install from CRAN directly

  Type the following command in R console to install it to /my/own/R-packages/ directly from CRAN:

  install.packages("mypkg", lib="/my/own/R-packages/")

To Load the library

• library("mypkg", lib.loc="/my/own/R-packages/")