![]() ![]() So now I’ll present the future of analytics software that must be true, because it feels so right to me personally. While ARIMA forecasting has an impressive mathematical foundation it’s always fun to follow Stephen Colbert’s approach: go from the gut. The 2015 figure in the Popularity paper and in the title of this blog post came from an exponential smoothing approach that did not match the rate of acceleration as well as the ARIMA approach does. However the part of the graph that I find most interesting is the shift from 2010 to 2011, which shows SPSS use still declining but at a much slower rate.Īny forecasting book will warn you of the dangers of looking too far beyond the data and I think these forecasts do just that. The forecast has taken a logical approach of focusing on the steeper decline from 2005 through 2010 and predicting that this year (2012) is the last time SPSS will see use in scholarly publications. > SPSS_forecast <- forecast(SPSS_fit, h=5) I find the SPSS prediction the most interesting: > SPSS_fit <- auto.arima(SPSS) I would bet Mitt Romney $10,000 that that is not going to happen! It appears that if the use of SAS continues to decline at its precipitous rate, all scholarly use of it will stop in 2014 (the number of articles published can’t be less than zero, so view the negatives as zero). If we follow the same steps for SAS we get: > SAS_fit <- auto.arima(SAS) We see that even if the use of SAS and SPSS were to remain at their current levels, R use would surpass their use in 2016 ( Point Forecast column where 18-22 represent years 2012 -2016). We can forecast the use of R using Rob Hyndman’s handy auto.arima function to forecast five years into the future: > library('forecast') Here is the data from Google Scholar: R SAS SPSS Let’s take a more detailed look at what the future may hold for R, SAS and SPSS Statistics. That was based on the data shown in Figure 7a, which I repeat here: In the latest update () I forecast that, if current trends continued, the use of the R software would exceed that of SAS for scholarly applications in 2015. I track this trend, and many others, in my article The Popularity of Data Analysis Software. As a result, the software used by professors and their students is likely to predict what the next generation of analysts will use for years to come. Learning to use a data analysis tool well takes significant effort, so people tend to continue using the tool they learned in college for much of their careers. ![]()
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