We can now forecast the cashflows to December 2025 and calculate the present value.

Analysis scripts are saved in the src folder. These are not automatically run when a project is opened, so it is helpful to load the project at the top of each script so it can be run as a standalone piece of analysis.

Create a new R script called analysis.R in the src folder with the following code and run it by clicking Source.

# Load project

# Model cashflows as a ARIMA(2,1,0) time series
cashflow_model = arima(cashflows, order=c(2,1,0))

# Create an 12 month forecast
forecast = forecast(cashflow_model, 12, level=c(80, 90, 95, 99))

# Plot the forecast
cf_plot = autoplot(forecast) +
  xlab("Year") +
  ylab("Cashflow") +
  ggtitle("12 month cashflow projection")

# Hold cashflow forecasts in a data frame
forecasts = data.frame(lower=c(cashflows, forecast$lower[,3]),
                       central=c(cashflows, forecast$mean),
                       upper=c(cashflows, forecast$upper[,3]))

# Set discount rate
disc = 0.03

# Discount cashflows
pv_lower = discount(forecasts[["lower"]], disc, 12)
pv_central = discount(forecasts[["central"]], disc, 12)
pv_upper = discount(forecasts[["upper"]], disc, 12)

This code models the cashflows as a time series using the arima() function and forecasts them over the following 12 months. A plot of the forecast is created using ggplot2 and stored as a variable. The cashflows are held in a data frame and the discount() helper function is used to discount them.

The results can be viewed by running the following code in the console.

> pv_lower
[1] 8695840
> pv_central
[1] 8851433
> pv_upper
[1] 9007027

Running > cf_plot will display a graph of the cashflows in the plots plane.


ggplot2 is a system for creating graphics in R. The cheat sheet here and the data visualisation chapter of R for Data Science covers how to use ggplot2 in detail.

You can use these resources to improve the plot, for example by formatting the axis and legend.