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:

421.15

Highest Value:

423.33

Lowest Value:

416.3

Adj. Closing Value:

418.07

  • The forecast plot shows the forecasted Microsoft 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 Microsoft stock.

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
May 2026 365.8282 349.4414 382.2150 340.7668 390.8896
Jun 2026 364.3915 339.8165 388.9665 326.8073 401.9757
Jul 2026 366.1181 334.0260 398.2102 317.0374 415.1987
Aug 2026 367.8447 329.6887 406.0006 309.4902 426.1992
Sep 2026 369.5712 326.1909 412.9515 303.2268 435.9157
Oct 2026 371.2978 323.2580 419.3376 297.8272 444.7683
Nov 2026 373.0243 320.7386 425.3101 293.0602 452.9885
Dec 2026 374.7509 318.5391 430.9627 288.7823 460.7195
Jan 2027 376.4775 316.5964 436.3585 284.8973 468.0577
Feb 2027 378.2040 314.8659 441.5421 281.3367 475.0713
Mar 2027 379.9306 313.3146 446.5466 278.0502 481.8109
Apr 2027 381.6571 311.9172 451.3971 274.9991 488.3152
  • In March of 2027, the Microsoft stock price is forecasted to be 380 dollars.

  • The width of the 80% prediction interval for this forecast in March of 2027 is 133 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/.