Last Update:
Monday, April 21, 2025
Opening Value:
49.08
Highest Value:
49.25
Lowest Value:
48.52
Adj. Closing Value:
48.86
- The forecast plot shows the forecasted New York Times 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 New York Times stock.
| Point Forecast | Lo 80 | Hi 80 | Lo 95 | Hi 95 | |
|---|---|---|---|---|---|
| May 2025 | 49.81538 | 46.44575 | 53.18502 | 44.66198 | 54.96879 |
| Jun 2025 | 49.81538 | 45.42410 | 54.20667 | 43.09949 | 56.53128 |
| Jul 2025 | 49.81538 | 44.59883 | 55.03194 | 41.83735 | 57.79342 |
| Aug 2025 | 49.81538 | 43.88736 | 55.74341 | 40.74926 | 58.88151 |
| Sep 2025 | 49.81538 | 43.25258 | 56.37819 | 39.77843 | 59.85234 |
| Oct 2025 | 49.81538 | 42.67399 | 56.95678 | 38.89357 | 60.73720 |
| Nov 2025 | 49.81538 | 42.13890 | 57.49187 | 38.07521 | 61.55556 |
| Dec 2025 | 49.81538 | 41.63874 | 57.99203 | 37.31029 | 62.32048 |
| Jan 2026 | 49.81538 | 41.16746 | 58.46330 | 36.58953 | 63.04124 |
| Feb 2026 | 49.81538 | 40.72058 | 58.91019 | 35.90608 | 63.72469 |
| Mar 2026 | 49.81538 | 40.29464 | 59.33613 | 35.25467 | 64.37610 |
| Apr 2026 | 49.81538 | 39.88696 | 59.74381 | 34.63117 | 64.99960 |
- In March of 2026, the New York Times stock price is forecasted to be 50 dollars.
- The width of the 80% prediction interval for this forecast in March of 2026 is 19 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 92.4% accurate.
This doesn’t guarantee that forecasts will be 92.4% 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.
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/.