Academic Journal of Computing & Information Science, 2026, 9(8); doi: 10.25236/AJCIS.2026.090815.
Yunting Wang
McGill University, Montréal, Canada, H3A 0G4
This paper presents two complementary applications of the Box-Jenkins approach to time-series modeling and forecasting. The first study analyzes annual global Internet penetration from 2005 to 2025 using a nonseasonal ARIMA framework. Successive differencing, formal stationarity testing, parameter screening, residual diagnostics, and chronological validation support the third-differenced moving-average model as the preferred specification relative to a fourth-differenced alternative. The second study analyzes monthly recreation visitation to Yellowstone National Park over the same period using a multiplicative SARIMA framework. Strong annual recurrence requires seasonal differencing and explicit seasonal dependence, and the evidence favors a model with one nonseasonal autoregressive term, one seasonal difference, and one seasonal moving-average term among the candidate specifications. Together, the studies demonstrate that model architecture must reflect sampling frequency and dependence structure: annual Internet penetration requires treatment of persistent nonseasonal trend, whereas monthly park visitation requires simultaneous representation of short-run and seasonal dynamics.
ARIMA, SARIMA, Box-Jenkins Methodology, Internet Penetration, National Park Visitation, Seasonality, Forecast Validation
Yunting Wang. Comparative Time-Series Modeling and Forecasting: Nonseasonal ARIMA for Global Internet Penetration and Seasonal ARIMA for Yellowstone Visitation. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 8: 118-127. https://doi.org/10.25236/AJCIS.2026.090815.
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