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:

681.36

Highest Value:

683.38

Lowest Value:

668

Adj. Closing Value:

670.91

  • The forecast plot shows the forecasted META Platforms Inc. 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 META Platforms Inc. stock.

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
Jan 2026 563.0973 530.1898 596.0048 512.7696 613.4250
Feb 2026 578.8349 529.4767 628.1930 503.3481 654.3216
Mar 2026 577.7800 518.9913 636.5687 487.8704 667.6895
Apr 2026 558.7489 494.2981 623.1997 460.1799 657.3178
May 2026 544.2165 471.7962 616.6369 433.4592 654.9739
Jun 2026 533.1196 451.2998 614.9393 407.9870 658.2521
Jul 2026 524.6458 432.7073 616.5843 384.0380 665.2536
Aug 2026 518.1752 415.8696 620.4808 361.7123 674.6381
Sep 2026 513.2342 400.5977 625.8707 340.9715 685.4968
Oct 2026 509.4612 386.6921 632.2303 321.7021 697.2203
Nov 2026 506.5801 373.9631 639.1971 303.7599 709.4003
Dec 2026 504.3801 362.2404 646.5197 286.9963 721.7639
  • In March of 2027, the META Platforms Inc. stock price is forecasted to be 507 dollars.

  • The width of the 80% prediction interval for this forecast in March of 2027 is 265 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 91.5% accurate.

  • This doesn’t guarantee that forecasts will be 91.5% 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/.