Last Update:

Monday, April 21, 2025

Opening Value:

98.77

Highest Value:

99.44

Lowest Value:

95.04

Adj. Closing Value:

96.91

  • The forecast plot shows the forecasted NVIDIA 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 NVIDIA stock.

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
May 2025 110.5837 105.0698 116.0975 102.15098 119.0164
Jun 2025 112.1470 103.4151 120.8789 98.79270 125.5013
Jul 2025 113.7103 102.5591 124.8615 96.65605 130.7646
Aug 2025 115.2737 102.0538 128.4936 95.05556 135.4918
Sep 2025 116.8370 101.7522 131.9218 93.76682 139.9072
Oct 2025 118.4003 101.5849 135.2157 92.68340 144.1173
Nov 2025 119.9637 101.5126 138.4147 91.74523 148.1821
Dec 2025 121.5270 101.5106 141.5434 90.91459 152.1394
Jan 2026 123.0903 101.5623 144.6184 90.16609 156.0146
Feb 2026 124.6537 101.6560 147.6514 89.48172 159.8256
Mar 2026 126.2170 101.7829 150.6512 88.84820 163.5858
Apr 2026 127.7803 101.9364 153.6243 88.25546 167.3052
  • In March of 2026, the NVIDIA stock price is forecasted to be 126 dollars.

  • The width of the 80% prediction interval for this forecast in March of 2026 is 49 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 90.3% accurate.

  • This doesn’t guarantee that forecasts will be 90.3% 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

Allaire, J., Teague, C., Scheidegger, C., Xie, Y., & Dervieux, C. (2024). Quarto (Version 1.5.57). doi.org/10.5281/zenodo.5960048 https://doi.org/10.5281/zenodo.5960048

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 (2022). magrittr: A Forward-Pipe Operator for R. R package version 2.0.3, 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 (2024). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.

R Core Team (2025). 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/.