Izvorni znanstveni članak
https://doi.org/10.24138/jcomss-2025-0298
Time-Series Forecasting with SARIMAX for Intent Prediction
Nagham Hachem
orcid.org/0009-0000-0676-2944
; LIGM, Univ. Gustave Eiffel, CNRS, ESIEE Paris, 93162 Marne-la-Vallee, France
*
Manh Cuong Nguyen
; LIGM, Univ. Gustave Eiffel, CNRS, ESIEE Paris, 93162 Marne-la-Vallee, France
Eric Renault
; LIGM, Univ. Gustave Eiffel, CNRS, ESIEE Paris, 93162 Marne-la-Vallee, France
* Dopisni autor.
Sažetak
By converting high-level user objectives into workable
settings, intent-based networking makes autonomous and
flexible network administration possible. However, without the
ability to proactively predict network intents across different
temporal granularities, its full potential is still constrained. Using
practical operational datasets, this study examines the use of
SARIMAX (Seasonal AutoRegressive Integrated Moving Average
with eXogenous variables) for multi-scale intent prediction. With
a fixed 1-day forecast horizon, we perform extensive trials under
two training regimes—8-day and 10-day historical data—across
prediction windows of 5, 10, 15, 20, and 25 minutes. Our
findings show a consistent trade-off between granularity and
stability: longer windows (20–25 minutes) produce smoother
forecasts at the cost of increased lag and decreased sensitivity
to sudden changes, while shorter windows (5–10 minutes) offer
greater responsiveness to real-time fluctuations but are prone
to noise. There were clear bias-variance trade-offs between the
two training durations, with the 5-minute window achieving the
lowest MAE and the 25-minute window minimizing RMSE. The
10-minute setup reliably balanced responsiveness and stability
despite regularly high MAPE values (over 280%), making it operationally
appropriate for IoT service orchestration and intentdriven
5G slice management. The study lays the groundwork for
future machine learning and hybrid model integration to improve
intent prediction in dynamic network contexts and emphasizes
the crucial role temporal aggregation plays in forecast success.
Ključne riječi
SARIMAX; ARMA Family; Intent; Forecasting
Hrčak ID:
348549
URI
Datum izdavanja:
31.3.2026.
Posjeta: 0 *