Floor of NYSE - Spencer Platt/Getty Images

This website incorporates forecasting skills from BUA 345 - Business Analytics into a dashboard presentation format.

Last Update:

Monday, April 20, 2026

Opening Value:

99

Highest Value:

99.73

Lowest Value:

98.3

Adj. Closing Value:

98.95

  • The forecast plot shows the forecasted Starbucks stock prices for the next 12 months.

  • The plot also shows the 80% prediction interval in dark purple and the 95% prediction interval in light purple.

  • The numeric values for these forecasted values and prediction intervals are shown in the next tab.

The table below shows the forecast values and 80% and 95% prediction intervals for the 12 requested forecasts for the Starbucks stock.

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
May 2026 91.76982 85.44719 98.09245 82.10019 101.4395
Jun 2026 95.24306 86.74369 103.74244 82.24439 108.2417
Jul 2026 95.06269 85.48301 104.64237 80.41184 109.7135
Aug 2026 94.61825 83.31781 105.91870 77.33572 111.9008
Sep 2026 94.61825 81.46262 107.77389 74.49844 114.7381
Oct 2026 94.61825 79.83848 109.39802 72.01455 117.2220
Nov 2026 94.61825 78.37595 110.86056 69.77780 119.4587
Dec 2026 94.61825 77.03465 112.20186 67.72645 121.5101
Jan 2027 94.61825 75.78865 113.44786 65.82086 123.4156
Feb 2027 94.61825 74.62014 114.61637 64.03377 125.2027
Mar 2027 94.61825 73.51623 115.72028 62.34549 126.8910
Apr 2027 94.61825 72.46726 116.76924 60.74124 128.4953
  • In March of 2027, the Starbucks stock price is forecasted to be 95 dollars.

  • The width of the 80% prediction interval for this forecast in March of 2027 is 42 dollars.
  • These three residual plots allow the analyst to examine the distribution of the residuals of the modeled time series.

  • Despite increasing volatility, our stock price model is estimated to be 94.4% accurate.

  • This doesn’t guarantee that forecasts will be 94.4% accurate but it does improve our chances of accurate forecasting.


This dashboard was created using Quarto in RStudio, and the R Language and Environment.

The dataset used to create this dashboard was downloaded from Yahoo Finance.

Software Citations

Arnold J (2024). ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’. R package version 5.1.0, https://CRAN.R-project.org/package=ggthemes.

Bache S, Wickham H (2025). magrittr: A Forward-Pipe Operator for R. doi:10.32614/CRAN.package.magrittr https://doi.org/10.32614/CRAN.package.magrittr, R package version 2.0.4, https://CRAN.R-project.org/package=magrittr.

Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2025). forecast: Forecasting functions for time series and linear models. R package version 8.24.0, https://pkg.robjhyndman.com/forecast/.

Hyndman RJ, Khandakar Y (2008). “Automatic time series forecasting: the forecast package for R.” Journal of Statistical Software, 27(3), 1-22. doi:10.18637/jss.v027.i03 https://doi.org/10.18637/jss.v027.i03.

Neuwirth E (2022). RColorBrewer: ColorBrewer Palettes. R package version 1.1-3, https://CRAN.R-project.org/package=RColorBrewer.

Posit team (2026). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.

Quarto Development Team. Quarto. Version 1.9.36. 2026. https://quarto.org/.

R Core Team (2026). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman

Vanderkam D, Allaire J, Owen J, Gromer D, Thieurmel B (2018). dygraphs: Interface to ‘Dygraphs’ Interactive Time Series Charting Library. R package version 1.1.1.6, https://CRAN.R-project.org/package=dygraphs.

Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.

Xie Y (2025). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.50, https://yihui.org/knitr/.

Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963

Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible Research in R. In Victoria Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595

Zhu H (2024). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.4.0, https://github.com/haozhu233/kableExtra, http://haozhu233.github.io/kableExtra/.