Forecasting Automotive Sales: Choose the Best Approach for Accurate Results

In summary, two automotive companies are trying to forecast the next year sales and are comparing different approaches for accuracy. I suggest using the moving average method for the first company and the naive method for the second company due to the limited data. Both methods should be evaluated using MAD, MSE, and MAPE.
  • #1
Jason000000
9
0
Two automotive companies are trying to forecast the next year sales. They try to select the best approach and tool to make the forecast as accurate as possible. Compare between the different approaches of forecasting and advise by return the one you suggest, and mention why did not you use the other

Year 1st company sales ( In millions) Month 2nd company sales( In millions)
2010 22 October 2017 42
2011 11 November 2017 47
2012 35 December 2017 37
2013 49
2014 52
2015 46
2017 50
 
Mathematics news on Phys.org
  • #2
Jason000000 said:
Two automotive companies are trying to forecast the next year sales. They try to select the best approach and tool to make the forecast as accurate as possible. Compare between the different approaches of forecasting and advise by return the one you suggest, and mention why did not you use the other

Year 1st company sales ( In millions) Month 2nd company sales( In millions)
2010 22 October 2017 42
2011 11 November 2017 47
2012 35 December 2017 37
2013 49
2014 52
2015 46
2017 50

Hi Jason.

So this is a time series problem most likely. It could be modeled in other ways but usually when we group by calendar month it involves time series. What topic(s) have you covered around this topic? What have you tried? Without some context it's hard to give guidance.
 
  • #3
Jameson said:
Hi Jason.

So this is a time series problem most likely. It could be modeled in other ways but usually when we group by calendar month it involves time series. What topic(s) have you covered around this topic? What have you tried? Without some context it's hard to give guidance.

Hi Jameson,

yes you are right it's a time series method .. one of many forecast technique ... and my guess it's the seasonality pattern of time series.
The problem is how to apply this seasonal pattern on both companies!

Year...1st company sales ( In millions)
2010.........22
2011.........11
2012.........35
2013.........49
2014.........52
2015.........46
2016.........48
2017.........50

Month......2nd company sales( In millions)
October 2017......42
November 2017......47
December 2017......37appreciate your input Jameson ...
 
  • #4
Jason000000 said:
Hi Jameson,

yes you are right it's a time series method .. one of many forecast technique ... and my guess it's the seasonality pattern of time series.
The problem is how to apply this seasonal pattern on both companies!

Year...1st company sales ( In millions)
2010.........22
2011.........11
2012.........35
2013.........49
2014.........52
2015.........46
2016.........48
2017.........50

Month......2nd company sales( In millions)
October 2017......42
November 2017......47
December 2017......37appreciate your input Jameson ...

my suggestion is to apply the "moving average" method of forecasting on company 1, and get the usual MAD, MSE & MAPE.
and as for the 2nd company.. due to very limited records I suggest applying the "naive" method. And also get the MAD, MSE & MAPE.

What do you think?
 
  • #5
Jason000000 said:
my suggestion is to apply the "moving average" method of forecasting on company 1, and get the usual MAD, MSE & MAPE.
and as for the 2nd company.. due to very limited records I suggest applying the "naive" method. And also get the MAD, MSE & MAPE.

What do you think?

Hi Jason,

This is data set is very, very small. Usually for time series we have a minimum of 30 points and ideally more like 100. So I think moving average is reasonable given this constraint. All of those metrics are fine to use. If this is for a course I'm really surprised by the lack of data. In my job we come across this issue sometimes but in a classroom they should try to construct usable data sets. Anyway, what do you get for the moving average? Over how many points do you propose to average?
 
  • #6
Jameson said:
Hi Jason,

This is data set is very, very small. Usually for time series we have a minimum of 30 points and ideally more like 100. So I think moving average is reasonable given this constraint. All of those metrics are fine to use. If this is for a course I'm really surprised by the lack of data. In my job we come across this issue sometimes but in a classroom they should try to construct usable data sets. Anyway, what do you get for the moving average? Over how many points do you propose to average?

Hey Jameson .. thanks for the input .. i will pass you what I achieved to check it out .. thanx
 
Last edited:

Related to Forecasting Automotive Sales: Choose the Best Approach for Accurate Results

1. What is the purpose of forecasting automotive sales?

The purpose of forecasting automotive sales is to predict the future demand for vehicles in the market. This allows car manufacturers and dealerships to make informed decisions about production, inventory, and marketing strategies.

2. What are the different approaches for forecasting automotive sales?

The three main approaches for forecasting automotive sales are time series analysis, regression analysis, and market research. Time series analysis uses historical sales data to predict future trends. Regression analysis uses various factors such as economic indicators and consumer behavior to forecast sales. Market research involves gathering data from surveys, focus groups, and other methods to gain insights into consumer preferences and buying patterns.

3. Which approach is the most accurate for forecasting automotive sales?

There is no one "best" approach for forecasting automotive sales. Each approach has its own strengths and limitations. Time series analysis is useful for short-term forecasting, while regression analysis can provide more comprehensive insights. Market research can offer valuable qualitative data, but may not always be reliable for quantitative predictions. The most accurate results can be achieved by combining multiple approaches and constantly adjusting the forecasting model based on real-time data.

4. What factors should be considered when forecasting automotive sales?

When forecasting automotive sales, it is important to consider various factors such as economic conditions, consumer trends, competition, and technological advancements. These factors can have a significant impact on the demand for vehicles and should be carefully analyzed to make accurate predictions.

5. How often should forecasting for automotive sales be done?

The frequency of forecasting for automotive sales depends on the specific needs of the business. Some companies may choose to do it on a monthly or quarterly basis, while others may only do it once or twice a year. It is important to regularly monitor sales data and adjust the forecasting model accordingly to stay up-to-date with market trends.

Similar threads

I Muon g-2 as a constraint on new physics
    • Beyond the Standard Models
Replies
18
Views
3K
Early universe not homogeneous
    • Cosmology
Replies
23
Views
5K
News Bush Honest & Trustworthy Until How Many Lies?
    • General Discussion
2
Replies
65
Views
8K

Hot Threads

Recent Insights

Back
Top