Last Update:

Wednesday, April 17, 2024

Opening Value:

94.61

Highest Value:

95.07

Lowest Value:

93.66

Adj. Closing Value:

94.84

  • The forecast plot shows the forecasted Nike stock prices for the next 24 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 24 requested forecasts for the Nike stock.

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
May 2024 91.09817 85.71791 96.47844 82.86977 99.32657
Jun 2024 91.92639 84.24850 99.60427 80.18408 103.66870
Jul 2024 89.88526 80.90590 98.86462 76.15252 103.61800
Aug 2024 90.09830 79.46378 100.73281 73.83421 106.36238
Sep 2024 89.60972 77.41466 101.80478 70.95899 108.26046
Oct 2024 90.68222 76.82141 104.54303 69.48394 111.88050
Nov 2024 90.90381 75.72854 106.07907 67.69525 114.11236
Dec 2024 91.30621 75.02681 107.58561 66.40901 116.20340
Jan 2025 90.80219 73.64731 107.95707 64.56606 117.03832
Feb 2025 90.50900 72.49854 108.51946 62.96438 118.05362
Mar 2025 90.15126 71.26997 109.03254 61.27483 119.02769
Apr 2025 90.32511 70.50613 110.14408 60.01459 120.63562
May 2025 90.55056 69.81248 111.28864 58.83440 122.26672
Jun 2025 90.84843 69.25053 112.44633 57.81730 123.87956
Jul 2025 90.85153 68.48937 113.21369 56.65156 125.05150
Aug 2025 90.72537 67.65696 113.79377 55.44529 126.00545
Sep 2025 90.50271 66.75106 114.25436 54.17770 126.82772
Oct 2025 90.42133 65.97361 114.86905 53.03177 127.81089
Nov 2025 90.46317 65.30937 115.61697 51.99375 128.93258
Dec 2025 90.60939 64.75685 116.46193 51.07134 130.14744
Jan 2026 90.70951 64.19026 117.22876 50.15181 131.26721
Feb 2026 90.72268 63.57358 117.87178 49.20172 132.24364
Mar 2026 90.64187 62.89086 118.39288 48.20037 133.08337
Apr 2026 90.55433 62.21183 118.89683 47.20822 133.90044
  • These three residual plots allow the analyst to examine the distribution of the residuals of the modeled time series.

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

Data 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 (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 (2024). forecast: Forecasting functions for time series and linear models. R package version 8.22.0, 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 (2024). 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 (2023). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.45, 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