Last Update:

Monday, April 21, 2025

Opening Value:

584

Highest Value:

593.84

Lowest Value:

555.6

Adj. Closing Value:

558.82

  • The forecast plot shows the forecasted Spotify 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 Spotify stock.

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
Feb 2025 556.5243 517.5614 595.4871 496.9357 616.1128
Mar 2025 561.3186 506.2168 616.4203 477.0476 645.5895
Apr 2025 566.1128 498.6272 633.5985 462.9025 669.3232
May 2025 570.9071 492.9814 648.8328 451.7300 690.0842
Jun 2025 575.7014 488.5778 662.8250 442.4574 708.9454
Jul 2025 580.4957 485.0566 675.9348 434.5342 726.4572
Aug 2025 585.2900 482.2040 688.3760 427.6335 742.9464
Sep 2025 590.0843 479.8807 700.2879 421.5424 758.6261
Oct 2025 594.8786 477.9900 711.7671 416.1129 773.6442
Nov 2025 599.6728 476.4615 722.8842 411.2373 788.1084
Dec 2025 604.4671 475.2420 733.6923 406.8343 802.1000
Jan 2026 609.2614 474.2901 744.2327 402.8406 815.6822
  • In March of 2026, the Spotify stock price is forecasted to be 604 dollars.

  • The width of the 80% prediction interval for this forecast in March of 2026 is 258 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 89.1% accurate.

  • This doesn’t guarantee that forecasts will be 89.1% 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/.