First, some context: the federal government provides a tax credit for electric vehicle manufacturers. For details, see here, but in short the full tax credit ($7,500) is available for consumers now through the quarter after the manufacturer sells and delivers 200,000 vehicles.
So, for example, if Tesla delivers its 200,000th vehicle on June 1, 2018 (Q2), then the full credit is available the remainder of that quarter plus one more full quarter (Q3 in this example). After that, the tax credit is cut in half for two additional quarters, and the cut in half again for two final quarters before the credit ends completely for the manufacturer.
Using R, I forecasted Tesla vehicle deliveries in the United States and then plotted it with ggplot2. Here’s how:
Thankfully I found a great site called InsideEVs that has collected or closely projected the quarterly US deliveries with their Plug-In Sales Scorecard. The historical data was not in crawlable tables (images instead), so instead of scraping the data I painstakingly copied everything over to Excel, but it was worth the effort since I knew I would not need to repeat this task.
After adding up models by month I ended up with this (displaying a condensed version here since there were about 100 rows):
Here is a link to the data if you would like to use it: Estimated Tesla Deliveries by Month Worksheet
I will ultimately need the data in quarters, but decided to forecast using months as the period because it gives me more data points and can more likely detect patters that way, like seasonality. I used the
forecast function in R in order to predict future deliveries based on previous months.
Assuming you have read the data into your IDE, I started by viewing my data in R to make sure it looked right and then activated the
graphics packages I knew I would need. I will be turning my data into a time series in R so that it can be understood by the
forecast function later on. It’s more or less adding meta data to your data table. If you’re working with monthly data, your frequency should be set to “12.” Before doing that, however, I knew I would not be needing the “Year” variable since this will be understood when I transform it into a time series in R, so I easily removed that entire column by setting it to NULL.
View(teslas) library(tseries) library(graphics) library(forecast) teslats$Year <- NULL View(teslats) teslats <- ts(teslas$`Cars Delivered`, start = c(2012, 1), end = c(2018, 5), frequency = 12) View(teslats)
Next, I plotted the data — just to get an idea of what it looked like on a graph. Good way to familiarize yourself with your data in a more visual way, since looking at just the numbers is difficult to discern.
There is a handy function in R that performs a seasonal decomposition of your data set. Let’s do that now and see what it shows us. We will call it “teslafit” so it doesn’t overwrite our other variables.
teslafit <- stl(teslats, s.window = "period") plot(teslafit)
If you remove the seasonality, it is pretty clear that there is still a strong positive trend. I’m not sure what information to take away from the seasonality, other than there appears to be a spike in the last month of each quarter and that is basically the pattern you are seeing in the graph above.
It’s important to get familiar with your data if you are really going to understand and forecast it. Two other useful ways to observe your data is with the
seasonplot, which both stem from the
forecast package. The seasonplot is essentially a year by year view of your data over a calendar year x-axis. They are both worth a gander, even though they are not necessary.
Okay, now it’s time to forecast. We are going to predict the next 19 months (through end of 2019) by adding our original time series data into the forecast function and then plot it out to see what we get.
forecast(teslafit, 19) plot(forecast(teslafit, 19))
The blue line is the middle of the forecast range, and what I’m interested in. It looks reasonable, and this is just for fun, so I’ll call it good. If this were a more important project, we would want to look at the accuracy of the forecast, try multiple forecasting methods, and compare them. To do this, you should become familiar with some of the common metrics to measure forecast accuracy like MAE and MASE.
Being a Tesla fan (and future Model 3 owner), I am aware of some outside factors that would impact this prediction. For one, it’s well documented that Elon Musk (of Tesla) wants to ramp up production of their Model 3 car in a significant way — and they even have a target of 5,000 per week, which I could have used in some way but did not given their history of missing targets.
A second factor is related to the possibility of Tesla strategically maximizing the federal tax credit for electric vehicles. My prediction (if you add up deliveries cumulatively by quarter) says they will reach the 200k threshold sometime in June. But it’s quite possible Tesla will hold back on U.S. deliveries so they instead reach the mark in July, which is a brand new quarter and thus would give more users an opportunity to take advantage of the federal tax credit program.
With that said, I continued on with my prediction knowing it was plausible. So the next thing I wanted to do is plot the quarterly data using ggplot2 in a way that the different tax credit phases were clearly displayed on top of the forecast line. Here is the end result:
In order to accomplish this, I used arguments in ggplot2 that allow you to shade different regions of the site and also added some text with specification on where the text should sit. Please note, I am using quarterly — not monthly — data now and you can get this in the Google Sheet I linked to above as a short cut. I named the data set “quarterly” and added a few additional columns in order to build the final product. Feel free to leave questions in the comments.
teslaplot <- ggplot(quarters) + geom_rect(aes(xmin='2018Q1', xmax='2018Q4', ymin=-Inf, ymax=Inf), fill="aquamarine", alpha=0.03) + geom_rect(aes(xmin='2018Q4', xmax='2019Q2', ymin=-Inf, ymax=Inf), fill="blue", alpha=0.03) + geom_rect(aes(xmin='2019Q2', xmax='2019Q4', ymin=-Inf, ymax=Inf), fill="darkmagenta", alpha=0.03) + geom_line(aes(Quarter, quarters$`Cumulative Sales`, group=1)) + labs(title='Tesla Federal Tax Credit Projection', x='Quarter', y='Vehicles Delivered') + scale_y_continuous(labels = comma) + geom_text(aes(x='2018Q3', y=200000, label='$7,500')) + geom_text(aes(x='2019Q1', y=200000, label='$3,750')) + geom_text(aes(x='2019Q3', y=200000, label='$1,875'))